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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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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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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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Molecular Identification of Mycobacterium Strains Responsible of Bovine Tuberculosis Cases in Bobo-Dioulasso Slaughterhouse, Burkina Faso
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作者 Mariétou Konate Aminata Fofana# +2 位作者 Yacouba Kouadima Aboubacar Sidiki Ouattara Adama Sanou 《Advances in Microbiology》 CAS 2024年第2期105-114,共10页
Bovine tuberculosis (bTB) is an endemic zoonosis significantly affects animal health in Burkina Faso. The primary causative agent is Mycobacterium tuberculosis (M. tuberculosis) complex, mainly M. bovis. Cattle are co... Bovine tuberculosis (bTB) is an endemic zoonosis significantly affects animal health in Burkina Faso. The primary causative agent is Mycobacterium tuberculosis (M. tuberculosis) complex, mainly M. bovis. Cattle are considered as natural reservoir of M. bovis. However, in Burkina Faso, the circulation of these strains remains poorly understood and documented. This study aimed to identify and characterize Mycobacterium strains from suspected carcasses during routine meat inspection at Bobo-Dioulasso refrigerated slaughterhouse. A prospective cross-sectional study was conducted from January 2021 to December 2022 on cases of seizures linked to suspected bovine tuberculosis. Microbiological and molecular analyzes were used for mycobacterial strain isolation and characterization. Out of 50 samples, 24% tested positive by microscopy and 12% by culture. Molecular analysis identified 6 strains of Mycobacteria, exclusively Mycobacterium bovis specifically the subspecies bovis (Mycobacterium bovis subsp bovis). In conclusion, M. bovis subsp bovis is the primary agent responsible for bovine tuberculosis in Bobo-Dioulasso. Continuous monitoring of mycobacterial strains is therefore necessary for the effective control of this pathology in the local cattle population. 展开更多
关键词 Bovine Tuberculosis Mycobacterium bovis Molecular identification Cattle Population Burkina Faso
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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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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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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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Research on Risk Identification and Industrial Governance of Digital Education Products Based on Data Annotation Technology
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作者 Tong Lili Zeng Jia +1 位作者 Di Ying Wang Nan 《China Communications》 SCIE CSCD 2024年第3期273-282,共10页
The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously pen... The social transformation brought aboutby digital technology is deeply impacting various industries.Digital education products, with core technologiessuch as 5G, AI, IoT (Internet of Things),etc., are continuously penetrating areas such as teaching,management, and evaluation. Apps, miniprograms,and emerging large-scale models are providingexcellent knowledge performance and flexiblecross-media output. However, they also exposerisks such as content discrimination and algorithmcommercialization. This paper conducts anevidence-based analysis of digital education productrisks from four dimensions: “digital resourcesinformationdissemination-algorithm design-cognitiveassessment”. It breaks through corresponding identificationtechnologies and, relying on the diverse characteristicsof governance systems, explores governancestrategies for digital education products from the threedomains of “regulators-developers-users”. 展开更多
关键词 digital education products industry governance risk 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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Stratigraphic identification using real-time drilling data
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作者 Minglong You Zhikai Hong +3 位作者 Fei Tan Hao Wen Zhanrong Zhang Jiahe Lv 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2024年第9期3452-3464,共13页
Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre... Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing method.The method can handle invalid drilling data generated during manual operations.The correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was established.The average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic interface.The average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,respectively.The results obtained using the present method were consistent with geotechnical survey results.The proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying. 展开更多
关键词 Monitoring while-drilling Drilling parameters Geotechnical stratigraphy Lithology identification
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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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Advancing healthcare through laboratory on a chip technology:Transforming microorganism identification and diagnostics
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作者 Carlos M Ardila 《World Journal of Clinical Cases》 SCIE 2025年第3期9-19,共11页
In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has... In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide. 展开更多
关键词 Laboratory-on-a-chip Microorganism identification DIAGNOSTICS Point-ofcare testing Biosensors
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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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A Novel 3D Gait Model for Subject Identification Robust against Carrying and Dressing Variations
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作者 Jian Luo Bo Xu +1 位作者 Tardi Tjahjadi Jian Yi 《Computers, Materials & Continua》 SCIE EI 2024年第7期235-261,共27页
Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3... Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes.This paper proposes a novel targeted 3-dimensional(3D)gait model(3DGait)represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model.The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing.The 3DGait recognitionmethod involves 2-dimensional(2D)to 3DGaitdata learningbasedon3Dvirtual samples,a semantic gait parameter estimation Long Short Time Memory(LSTM)network(3D-SGPE-LSTM),a feature fusion deep model based on a multi-set canonical correlation analysis,and SoftMax recognition network.First,a sensory experiment based on 3D body shape and pose deformation with 3D virtual dressing is used to fit 3DGait onto the given 2D gait images.3Dinterpretable semantic parameters control the 3D morphing and dressing involved.Similarity degree measurement determines the semantic descriptors of 2D gait images of subjects with various shapes,poses and styles.Second,using the 2D gait images as input and the subjects’corresponding 3D semantic descriptors as output,an end-to-end 3D-SGPE-LSTM is constructed and trained.Third,body shape,pose and external gait factors(3D-eFactors)are estimated using the 3D-SGPE-LSTM model to create a set of interpretable gait descriptors to represent the 3DGait Model,i.e.,3D intrinsic semantic shape descriptor(3DShape);3D skeleton-based gait pose descriptor(3D-Pose)and 3D dressing with other 3D-eFators.Finally,the 3D-Shape and 3D-Pose descriptors are coupled to a unified pattern space by learning prior knowledge from the 3D-eFators.Practical research on CASIA B,CMU MoBo,TUM GAID and GPJATK databases shows that 3DGait is robust against object carrying and dressing variations,especially under multi-cross variations. 展开更多
关键词 Gait recognition human identification three-dimensional gait canonical correlation analysis
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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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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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Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2
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作者 Zhilin Li Yuxin Li +5 位作者 Chunyu Yan Peng Yan Xiutong Li Mei Yu Tingchi Wen Benliang Xie 《Computers, Materials & Continua》 SCIE EI 2024年第7期679-694,共16页
Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existi... Diseases in tea trees can result in significant losses in both the quality and quantity of tea production.Regular monitoring can help to prevent the occurrence of large-scale diseases in tea plantations.However,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring equipment.This paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these challenges.The proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions accurately.Secondly,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease scales.Finally,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational complexity.Experimental results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments. 展开更多
关键词 Disease identification coordinate attention mechanism multi-scale feature extraction model pruning
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