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Pollution source identification methods and remediation technologies of groundwater: A review
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作者 Ya-ci Liu Yu-hong Fei +2 位作者 Ya-song Li Xi-lin Bao Peng-wei Zhang 《China Geology》 CAS CSCD 2024年第1期125-137,共13页
Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identi... Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies. 展开更多
关键词 Groundwater pollution identification of pollution sources Geophysical exploration identification Geochemistry identification Isotopic tracing Numerical modeling Remediation technology Hydrogeological conditions Hydrogeological survey engineering
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DC-FIPD: Fraudulent IP Identification Method Based on Homology Detection
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作者 Yuanyuan Ma Ang Chen +3 位作者 Cunzhi Hou Ruixia Jin Jinghui Zhang Ruixiang Li 《Computers, Materials & Continua》 SCIE EI 2024年第11期3301-3323,共23页
Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.Ho... Currently,telecom fraud is expanding from the traditional telephone network to the Internet,and identifying fraudulent IPs is of great significance for reducing Internet telecom fraud and protecting consumer rights.However,existing telecom fraud identification methods based on blacklists,reputation,content and behavioral characteristics have good identification performance in the telephone network,but it is difficult to apply to the Internet where IP(Internet Protocol)addresses change dynamically.To address this issue,we propose a fraudulent IP identification method based on homology detection and DBSCAN(Density-Based Spatial Clustering of Applications with Noise)clustering(DC-FIPD).First,we analyze the aggregation of fraudulent IP geographies and the homology of IP addresses.Next,the collected fraudulent IPs are clustered geographically to obtain the regional distribution of fraudulent IPs.Then,we constructed the fraudulent IP feature set,used the genetic optimization algorithm to determine the weights of the fraudulent IP features,and designed the calculation method of the IP risk value to give the risk value threshold of the fraudulent IP.Finally,the risk value of the target IP is calculated and the IP is identified based on the risk value threshold.Experimental results on a real-world telecom fraud detection dataset show that the DC-FIPD method achieves an average identification accuracy of 86.64%for fraudulent IPs.Additionally,the method records a precision of 86.08%,a recall of 45.24%,and an F1-score of 59.31%,offering a comprehensive evaluation of its performance in fraud detection.These results highlight the DC-FIPD method’s effectiveness in addressing the challenges of fraudulent IP identification. 展开更多
关键词 Fraudulent IP identification homology detection CLUSTERING genetic optimization algorithm telecom fraud identification
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Simultaneous Identification of Thermophysical Properties of Semitransparent Media Using a Hybrid Model Based on Artificial Neural Network and Evolutionary Algorithm
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作者 LIU Yang HU Shaochuang 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2024年第4期458-475,共18页
A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductiv... A hybrid identification model based on multilayer artificial neural networks(ANNs) and particle swarm optimization(PSO) algorithm is developed to improve the simultaneous identification efficiency of thermal conductivity and effective absorption coefficient of semitransparent materials.For the direct model,the spherical harmonic method and the finite volume method are used to solve the coupled conduction-radiation heat transfer problem in an absorbing,emitting,and non-scattering 2D axisymmetric gray medium in the background of laser flash method.For the identification part,firstly,the temperature field and the incident radiation field in different positions are chosen as observables.Then,a traditional identification model based on PSO algorithm is established.Finally,multilayer ANNs are built to fit and replace the direct model in the traditional identification model to speed up the identification process.The results show that compared with the traditional identification model,the time cost of the hybrid identification model is reduced by about 1 000 times.Besides,the hybrid identification model remains a high level of accuracy even with measurement errors. 展开更多
关键词 semitransparent medium coupled conduction-radiation heat transfer thermophysical properties simultaneous identification multilayer artificial neural networks(ANNs) evolutionary algorithm hybrid identification model
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Fluid identification and tight oil layer classification for the southwestern Mahu Sag,Junggar Basin using NMR logging-based spectrum decomposition
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作者 Wenbiao Huang Yibing Guo +4 位作者 Jun Qin Zhongchen Ba Zongbin Zhang Luning Bai Heng Li 《Energy Geoscience》 EI 2024年第2期62-69,共8页
The intricate distribution of oil and water in tight rocks makes pinpointing oil layers challenging.While conventional identification methods offer potential solutions,their limited accuracy precludes them from being ... The intricate distribution of oil and water in tight rocks makes pinpointing oil layers challenging.While conventional identification methods offer potential solutions,their limited accuracy precludes them from being effective in their applications to unconventional reservoirs.This study employed nuclear magnetic resonance(NMR)spectrum decomposition to dissect the NMR T_(2)spectrum into multiple subspectra.Furthermore,it employed laboratory NMR experiments to ascertain the fluid properties of these sub-spectra,aiming to enhance identification accuracy.The findings indicate that fluids of distinct properties overlap in the T_(2)spectra,with bound water,movable water,bound oil,and movable oil appearing sequentially from the low-value zone to the high-value zone.Consequently,an oil layer classification scheme was proposed,which considers the physical properties of reservoirs,oil-bearing capacity,and the characteristics of both mobility and the oil-water two-phase flow.When applied to tight oil layer identification,the scheme's outcomes align closely with actual test results.A horizontal well,deployed based on these findings,has produced high-yield industrial oil flow,underscoring the precision and dependability of this new approach. 展开更多
关键词 Nuclear magnetic resonance Fluid identification Oil layer identification NMR T_(2)spectrum decomposition Normal distribution simulation
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Pharmacognosy research and identification of Euphorbia prostrata Ait.
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作者 Jia Yan Wen-Feng Weng Sheng-Guo Ji 《TMR Modern Herbal Medicine》 CAS 2024年第1期19-25,共7页
Background:Euphorbia prostrata Ait.is an annual herb widely distributed in the southern region of China with great medical values on Anti-inflammation,insect repellent,treatment of diarrhea.Despite its extensive uses ... Background:Euphorbia prostrata Ait.is an annual herb widely distributed in the southern region of China with great medical values on Anti-inflammation,insect repellent,treatment of diarrhea.Despite its extensive uses as a traditional Chinese medicine,no systematic research on the identification of E.prostrata has been reported.Methods:The study aimed to establish an accurate identification system for E.prostrata through traditional pharmacognostical methods,including botanical origin,morphological characters,medicinal material characters,microscopic characters,physicochemical parameters determination,phytochemical screening,and DNA barcoding analysis.Results:Physicochemical results show that this plant likely contains flavonoids,anthraquinones,and other substances.The ITS loci of the nuclear genome and psbA-trnH loci of the chloroplast genome were selected and evaluated,which were the most variable loci.Conclusion:The findings of this study are expected to contribute to the development of species identification,as well as provide references for authenticity identification,genetic relationship analysis,and further utilization of E.prostrata. 展开更多
关键词 Euphorbia prostrata Ait. microscopic identification physico-chemical identification ITS sequence psbA-trnH sequence
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Systematic Evaluation of Pharmacognostic Identification of Polygonum capitatum 被引量:1
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作者 Bo TU Xu ZHANG +3 位作者 Minghui HE Shanggao LIAO Yongqin ZENG Yan LIN 《Medicinal Plant》 CAS 2023年第4期9-13,共5页
[Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical ... [Objectives] To investigate the systematic evaluation of pharmacognostic identification of Polygonum capitatum . [Methods] 10 batches of P. capitatum cultivated in Guizhou were chosen for plant samples. Macroscopical identification was conducted on plant roots, stems, leaves, flowers and fruits. The P. capitatum powder was processed for physical and chemical distinction by FeCl 3 chromogenic reaction, hydrochloric acid magnesium powder reaction, AlCl 3 color development reaction and thin-layer chromatography.Microscope identification was carried out on the powder. Plant genome DNeasy Plant Kit was adopted for DNA molecular marker identification. [Results] The results showed that the stem of P. capitatum was tufted, the leaves were oval, 2 to 5 cm long, and 1 to 2 cm wide;the leaf apex was acute and cuneate at the base, the inflorescence was capitate, paired or solitary;the raceme was erect and nearly spherical, and the perianth was light red. Furthermore, for the chromogenic reaction of FeCl 3 ethanol extract of P. capitatum , appeared blue and turned to dark blue after long time storing at room temperature. For the reaction of hydrochloric acid magnesium powder, the alcohol extract of P. capitatum , exhibited deep red. In the color reaction of AlCl 3, the alcohol extract revealed yellow fluorescence under 360 nm UV lamp. Microscope identification of the powder displayed pollen grains, crystal sheath fibers, cellulose, vessels, starch grains, cork cells, and other characteristic fragments. In addition, DNA barcoding electrophoresis results showed that P. capitatum showed a clear and bright single band near 500 bp, and further sequencing results showed that the sequence differences were mainly concentrated in ITS1 and ITS2 region. [Conclusions] Systematic evaluation for the identification of P. capitatum is established, which combines with macroscopic identification, physicochemical identification, powder microscope identification, and DNA molecular identification. Finally, the original medicinal material is identified as P. capitatum Buch.-Ham. ex D. Don. 展开更多
关键词 Chinese herbal medicine Polygonum capitatum Pharmacognostic identification Character identification Physical and chemical identification Microscopic identification DNA molecular marker identification
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Identification and evaluation of shale oil micromigration and its petroleum geological significance 被引量:2
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作者 HU Tao JIANG Fujie +10 位作者 PANG Xiongqi LIU Yuan WU Guanyun ZHOU Kuo XIAO Huiyi JIANG Zhenxue LI Maowen JIANG Shu HUANG Liliang CHEN Dongxia MENG Qingyang 《Petroleum Exploration and Development》 SCIE 2024年第1期127-140,共14页
Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil... Taking the Lower Permian Fengcheng Formation shale in Mahu Sag of Junggar Basin,NW China,as an example,core observation,test analysis,geological analysis and numerical simulation were applied to identify the shale oil micro-migration phenomenon.The hydrocarbon micro-migration in shale oil was quantitatively evaluated and verified by a self-created hydrocarbon expulsion potential method,and the petroleum geological significance of shale oil micro-migration evaluation was determined.Results show that significant micro-migration can be recognized between the organic-rich lamina and organic-poor lamina.The organic-rich lamina has strong hydrocarbon generation ability.The heavy components of hydrocarbon preferentially retained by kerogen swelling or adsorption,while the light components of hydrocarbon were migrated and accumulated to the interbedded felsic or carbonate organic-poor laminae as free oil.About 69% of the Fengcheng Formation shale samples in Well MY1 exhibit hydrocarbon charging phenomenon,while 31% of those exhibit hydrocarbon expulsion phenomenon.The reliability of the micro-migration evaluation results was verified by combining the group components based on the geochromatography effect,two-dimension nuclear magnetic resonance analysis,and the geochemical behavior of inorganic manganese elements in the process of hydrocarbon migration.Micro-migration is a bridge connecting the hydrocarbon accumulation elements in shale formations,which reflects the whole process of shale oil generation,expulsion and accumulation,and controls the content and composition of shale oil.The identification and evaluation of shale oil micro-migration will provide new perspectives for dynamically differential enrichment mechanism of shale oil and establishing a“multi-peak model in oil generation”of shale. 展开更多
关键词 shale oil micro-migration identification micro-migration evaluation Junggar Basin Mahu Sag Lower Permian Fengcheng Formation hydrocarbon expulsion potential method
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Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression 被引量:1
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作者 DONG Wen-bo ZHOU Li +2 位作者 DING Shi-feng WANG Ai-ming CAI Jin-yan 《China Ocean Engineering》 SCIE EI CSCD 2024年第2期313-325,共13页
Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ... Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second. 展开更多
关键词 ice channel ship navigation identification image segmentation corner point regression
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Dynamic Hand Gesture-Based Person Identification Using Leap Motion and Machine Learning Approaches 被引量:1
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作者 Jungpil Shin Md.AlMehedi Hasan +2 位作者 Md.Maniruzzaman Taiki Watanabe Issei Jozume 《Computers, Materials & Continua》 SCIE EI 2024年第4期1205-1222,共18页
Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, f... Person identification is one of the most vital tasks for network security. People are more concerned about theirsecurity due to traditional passwords becoming weaker or leaking in various attacks. In recent decades, fingerprintsand faces have been widely used for person identification, which has the risk of information leakage as a resultof reproducing fingers or faces by taking a snapshot. Recently, people have focused on creating an identifiablepattern, which will not be reproducible falsely by capturing psychological and behavioral information of a personusing vision and sensor-based techniques. In existing studies, most of the researchers used very complex patternsin this direction, which need special training and attention to remember the patterns and failed to capturethe psychological and behavioral information of a person properly. To overcome these problems, this researchdevised a novel dynamic hand gesture-based person identification system using a Leap Motion sensor. Thisstudy developed two hand gesture-based pattern datasets for performing the experiments, which contained morethan 500 samples, collected from 25 subjects. Various static and dynamic features were extracted from the handgeometry. Randomforest was used to measure feature importance using the Gini Index. Finally, the support vectormachinewas implemented for person identification and evaluate its performance using identification accuracy. Theexperimental results showed that the proposed system produced an identification accuracy of 99.8% for arbitraryhand gesture-based patterns and 99.6% for the same dynamic hand gesture-based patterns. This result indicatedthat the proposed system can be used for person identification in the field of security. 展开更多
关键词 Person identification leap motion hand gesture random forest support vector machine
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Applying an Improved Dung Beetle Optimizer Algorithm to Network Traffic Identification 被引量:1
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作者 Qinyue Wu Hui Xu Mengran Liu 《Computers, Materials & Continua》 SCIE EI 2024年第3期4091-4107,共17页
Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexi... Network traffic identification is critical for maintaining network security and further meeting various demands of network applications.However,network traffic data typically possesses high dimensionality and complexity,leading to practical problems in traffic identification data analytics.Since the original Dung Beetle Optimizer(DBO)algorithm,Grey Wolf Optimization(GWO)algorithm,Whale Optimization Algorithm(WOA),and Particle Swarm Optimization(PSO)algorithm have the shortcomings of slow convergence and easily fall into the local optimal solution,an Improved Dung Beetle Optimizer(IDBO)algorithm is proposed for network traffic identification.Firstly,the Sobol sequence is utilized to initialize the dung beetle population,laying the foundation for finding the global optimal solution.Next,an integration of levy flight and golden sine strategy is suggested to give dung beetles a greater probability of exploring unvisited areas,escaping from the local optimal solution,and converging more effectively towards a global optimal solution.Finally,an adaptive weight factor is utilized to enhance the search capabilities of the original DBO algorithm and accelerate convergence.With the improvements above,the proposed IDBO algorithm is then applied to traffic identification data analytics and feature selection,as so to find the optimal subset for K-Nearest Neighbor(KNN)classification.The simulation experiments use the CICIDS2017 dataset to verify the effectiveness of the proposed IDBO algorithm and compare it with the original DBO,GWO,WOA,and PSO algorithms.The experimental results show that,compared with other algorithms,the accuracy and recall are improved by 1.53%and 0.88%in binary classification,and the Distributed Denial of Service(DDoS)class identification is the most effective in multi-classification,with an improvement of 5.80%and 0.33%for accuracy and recall,respectively.Therefore,the proposed IDBO algorithm is effective in increasing the efficiency of traffic identification and solving the problem of the original DBO algorithm that converges slowly and falls into the local optimal solution when dealing with high-dimensional data analytics and feature selection for network traffic identification. 展开更多
关键词 Network security network traffic identification data analytics feature selection dung beetle optimizer
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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm
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作者 Tie Yan Rui Xu +2 位作者 Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期1135-1148,共14页
Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face ... Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation. 展开更多
关键词 Intelligent drilling Closed-loop drilling Lithology identification Random forest algorithm Feature extraction
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Dynamics Modeling and Parameter Identification for a Coupled-Drive Dual-Arm Nursing Robot
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作者 Hao Lu Zhiqiang Yang +2 位作者 Deliang Zhu Fei Deng Shijie Guo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第4期243-257,共15页
A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well... A dual-arm nursing robot can gently lift patients and transfer them between a bed and a wheelchair.With its lightweight design,high load-bearing capacity,and smooth surface,the coupled-drive joint is particularly well suited for these robots.However,the coupled nature of the joint disrupts the direct linear relationship between the input and output torques,posing challenges for dynamic modeling and practical applications.This study investigated the transmission mechanism of this joint and employed the Lagrangian method to construct a dynamic model of its internal dynamics.Building on this foundation,the Newton-Euler method was used to develop a dynamic model for the entire robotic arm.A continuously differentiable friction model was incorporated to reduce the vibrations caused by speed transitions to zero.An experimental method was designed to compensate for gravity,inertia,and modeling errors to identify the parameters of the friction model.This method establishes a mapping relationship between the friction force and motor current.In addition,a Fourier series-based excitation trajectory was developed to facilitate the identification of the dynamic model parameters of the robotic arm.Trajectory tracking experiments were conducted during the experimental validation phase,demonstrating the high accuracy of the dynamic model and the parameter identification method for the robotic arm.This study presents a dynamic modeling and parameter identification method for coupled-drive joint robotic arms,thereby establishing a foundation for motion control in humanoid nursing robots. 展开更多
关键词 Nursing-care robot Coupled-drive joint Dynamic modeling Parameter identification
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Dynamic Identification and Evolution of Urban-suburban-rural Transition Zones Based on the Blender of Natural and Humanistic Factors: A Case Study of Chengdu, China
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作者 DENG Wei JIANG Zhenyuan +3 位作者 ZHANG Shaoyao REN Ping ZHANG Hao WANG Zhanyun 《Chinese Geographical Science》 SCIE CSCD 2024年第5期791-809,共19页
Urban-suburban-rural(U-S-R)zones exhibit distinctive transitional characteristics in interaction between human and nature.U-S-R transition zones(U-S-RTZ)are also highlighting the function diversity and landscape heter... Urban-suburban-rural(U-S-R)zones exhibit distinctive transitional characteristics in interaction between human and nature.U-S-R transition zones(U-S-RTZ)are also highlighting the function diversity and landscape heterogeneity across territorial spaces.As a super megacity in western China,Chengdu’s rapid urbanization has driven the evolution of U-S-R spaces,resulting in a sequential structure.To promote the high-quality spatial development of urban-rural region in a structured and efficient manner,it is essential to con-duct a scientific examination of the multidimensional interconnection within the U-S-RTZ framework.By proposing a novel identifica-tion method of U-S-RTZ and taking Chengdu,China as a case study,grounded in a blender of natural and humanistic factors,this study quantitatively delineated and explored the spatial evolutions of U-S-RTZ and stated the optimization orientation and sustainable devel-opment strategies of the production-living-ecological spaces along the U-S-R gradients.The results show that:1)it is suitable for the quantitative analysis of U-S-RTZ by established three-dimensional identification system in this study.2)In 1990-2020,the urban-sub-urban transition zones(U-STZ)in Chengdu have continuously undergone a substantial increase,and the scale of the suburban-rural transition zones(S-RTZ)has continued to expand slightly,while the space of rural-ecological transition zones(R-ETZ)has noticeably compressed.3)The landuse dynamics within U-S-RTZ has gradually increased in 1990-2020.The main direction of landuse transition was from farmland to construction land or woodlands,with the expansion of construction land being the most significant.4)R-ETZ primarily focus on ecological functions,and there is a trade-off relationship between the production-ecological function within the S-RTZ,and in the U-STZ,production-living composite functions are prioritized.This study emphasizes the importance of elastic planning and precise governance within the U-S-RTZ in a rapid urbanization region,particularly highlighting the role of suburbs as landscape corridors and service hubs in urban-rural integration.It elucidates to the practical implications for achieving high-quality development of integrated U-S-R territorial spaces. 展开更多
关键词 transition zone urban-suburban-rural zones spatial identification evolution mechanism Chengdu China
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Digital Image Steganographer Identification:A Comprehensive Survey
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作者 Qianqian Zhang Yi Zhang +2 位作者 Yuanyuan Ma Yanmei Liu Xiangyang Luo 《Computers, Materials & Continua》 SCIE EI 2024年第10期105-131,共27页
The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse.Identifying steganographer is essential in tracing secret information origins ... The rapid development of the internet and digital media has provided convenience while also posing a potential risk of steganography abuse.Identifying steganographer is essential in tracing secret information origins and preventing illicit covert communication online.Accurately discerning a steganographer from many normal users is challenging due to various factors,such as the complexity in obtaining the steganography algorithm,extracting highly separability features,and modeling the cover data.After extensive exploration,several methods have been proposed for steganographer identification.This paper presents a survey of existing studies.Firstly,we provide a concise introduction to the research background and outline the issue of steganographer identification.Secondly,we present fundamental concepts and techniques that establish a general framework for identifying steganographers.Within this framework,state-of-the-art methods are summarized from five key aspects:data acquisition,feature extraction,feature optimization,identification paradigm,and performance evaluation.Furthermore,theoretical and experimental analyses examine the advantages and limitations of these existing methods.Finally,the survey highlights outstanding issues in image steganographer identification that deserve further research. 展开更多
关键词 Information hiding STEGANALYSIS steganographer identification STEGANOGRAPHY covert communication SURVEY
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Model Parameters Identification and Backstepping Control of Lower Limb Exoskeleton Based on Enhanced Whale Algorithm
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作者 Yan Shi Jiange Kou +2 位作者 Zhenlei Chen Yixuan Wang Qing Guo 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2024年第2期100-114,共15页
Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of i... Exoskeletons generally require accurate dynamic models to design the model-based controller conveniently under the human-robot interaction condition.However,due to unknown model parameters such as the mass,moment of inertia and mechanical size,the dynamic model of exoskeletons is difficult to construct.Hence,an enhanced whale optimization algorithm(EWOA)is proposed to identify the exoskeleton model parameters.Meanwhile,the periodic excitation trajectories are designed by finite Fourier series to input the desired position demand of exoskeletons with mechanical physical constraints.Then a backstepping controller based on the identified model is adopted to improve the human-robot wearable comfortable performance under cooperative motion.Finally,the proposed Model parameters identification and control are verified by a two-DOF exoskeletons platform.The knee joint motion achieves a steady-state response after 0.5 s.Meanwhile,the position error of hip joint response is less than 0.03 rad after 0.9 s.In addition,the steady-state human-robot interaction torque of the two joints is constrained within 15 N·m.This research proposes a whale optimization algorithm to optimize the excitation trajectory and identify model parameters.Furthermore,an enhanced mutation strategy is adopted to avoid whale evolution’s unsatisfactory local optimal value. 展开更多
关键词 Parameter identification Enhanced whale optimization algorithm(EWOA) BACKSTEPPING Human-robot interaction Lower limb exoskeleton
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Identification of reservoir types in deep carbonates based on mixedkernel machine learning using geophysical logging data
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作者 Jin-Xiong Shi Xiang-Yuan Zhao +3 位作者 Lian-Bo Zeng Yun-Zhao Zhang Zheng-Ping Zhu Shao-Qun Dong 《Petroleum Science》 SCIE EI CAS CSCD 2024年第3期1632-1648,共17页
Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analy... Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces.Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency,which cannot accurately reflect the nonlinear relationship between reservoir types and logging data.Recently,the kernel Fisher discriminant analysis(KFD),a kernel-based machine learning technique,attracts attention in many fields because of its strong nonlinear processing ability.However,the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well,especially for highly complex data cases.To address this issue,in this study,a mixed kernel Fisher discriminant analysis(MKFD)model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin,China.The MKFD model was trained and tested with 453 datasets from 7 coring wells,utilizing GR,CAL,DEN,AC,CNL and RT logs as input variables.The particle swarm optimization(PSO)was adopted for hyper-parameter optimization of MKFD model.To evaluate the model performance,prediction results of MKFD were compared with those of basic-kernel based KFD,RF and SVM models.Subsequently,the built MKFD model was applied in a blind well test,and a variable importance analysis was conducted.The comparison and blind test results demonstrated that MKFD outperformed traditional KFD,RF and SVM in the identification of reservoir types,which provided higher accuracy and stronger generalization.The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates. 展开更多
关键词 Reservoir type identification Geophysical logging data Kernel Fisher discriminantanalysis Mixedkernel function Deep carbonates
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Optimizing near-carbon-free nuclear energy systems:advances in reactor operation digital twin through hybrid machine learning algorithms for parameter identification and state estimation
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作者 Li‑Zhan Hong He‑Lin Gong +3 位作者 Hong‑Jun Ji Jia‑Liang Lu Han Li Qing Li 《Nuclear Science and Techniques》 SCIE EI CAS CSCD 2024年第8期177-203,共27页
Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,... Accurate and efficient online parameter identification and state estimation are crucial for leveraging digital twin simulations to optimize the operation of near-carbon-free nuclear energy systems.In previous studies,we developed a reactor operation digital twin(RODT).However,non-differentiabilities and discontinuities arise when employing machine learning-based surrogate forward models,challenging traditional gradient-based inverse methods and their variants.This study investigated deterministic and metaheuristic algorithms and developed hybrid algorithms to address these issues.An efficient modular RODT software framework that incorporates these methods into its post-evaluation module is presented for comprehensive comparison.The methods were rigorously assessed based on convergence profiles,stability with respect to noise,and computational performance.The numerical results show that the hybrid KNNLHS algorithm excels in real-time online applications,balancing accuracy and efficiency with a prediction error rate of only 1%and processing times of less than 0.1 s.Contrastingly,algorithms such as FSA,DE,and ADE,although slightly slower(approximately 1 s),demonstrated higher accuracy with a 0.3%relative L_2 error,which advances RODT methodologies to harness machine learning and system modeling for improved reactor monitoring,systematic diagnosis of off-normal events,and lifetime management strategies.The developed modular software and novel optimization methods presented offer pathways to realize the full potential of RODT for transforming energy engineering practices. 展开更多
关键词 Parameter identification State estimation Reactor operation digital twin Reduced order model Inverse problem
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Quaternion-Based Adaptive Trajectory Tracking Control of a Rotor-Missile with Unknown Parameters Identification
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作者 Jie Zhao Zhongjiao Shi +1 位作者 Yuchen Wang Wei Wang 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第1期375-386,共12页
This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncerta... This paper investigates the adaptive trajectory tracking control problem and the unknown parameter identification problem of a class of rotor-missiles with parametric system uncertainties.First,considering the uncertainty of structural and aerodynamic parameters,the six-degree-of-freedom(6Do F) nonlinear equations describing the position and attitude dynamics of the rotor-missile are established,respectively,in the inertial and body-fixed reference frames.Next,a hierarchical adaptive trajectory tracking controller that can guarantee closed-loop stability is proposed according to the cascade characteristics of the 6Do F dynamics.Then,a memory-augmented update rule of unknown parameters is proposed by integrating all historical data of the regression matrix.As long as the finitely excited condition is satisfied,the precise identification of unknown parameters can be achieved.Finally,the validity of the proposed trajectory tracking controller and the parameter identification method is proved through Lyapunov stability theory and numerical simulations. 展开更多
关键词 Rotor-missile Adaptive control Parameter identification Quaternion control
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The Machine Learning Ensemble for Analyzing Internet of Things Networks:Botnet Detection and Device Identification
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作者 Seung-Ju Han Seong-Su Yoon Ieck-Chae Euom 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第11期1495-1518,共24页
The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a re... The rapid proliferation of Internet of Things(IoT)technology has facilitated automation across various sectors.Nevertheless,this advancement has also resulted in a notable surge in cyberattacks,notably botnets.As a result,research on network analysis has become vital.Machine learning-based techniques for network analysis provide a more extensive and adaptable approach in comparison to traditional rule-based methods.In this paper,we propose a framework for analyzing communications between IoT devices using supervised learning and ensemble techniques and present experimental results that validate the efficacy of the proposed framework.The results indicate that using the proposed ensemble techniques improves accuracy by up to 1.7%compared to singlealgorithm approaches.These results also suggest that the proposed framework can flexibly adapt to general IoT network analysis scenarios.Unlike existing frameworks,which only exhibit high performance in specific situations,the proposed framework can serve as a fundamental approach for addressing a wide range of issues. 展开更多
关键词 Internet of Things machine learning traffic analysis botnet detection device identification
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A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization
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作者 DENG Song PAN Haoyu +5 位作者 LI Chaowei YAN Xiaopeng WANG Jiangshuai SHI Lin PEI Chunyu CAI Meng 《Acta Geologica Sinica(English Edition)》 SCIE CAS CSCD 2024年第2期518-530,共13页
In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on ... In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process. 展开更多
关键词 mud logging data real-time lithological identification improved crow search algorithm petroleum geological exploration SMOTE-Tomek
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