Deserts are one of the major landforms on the Earth. While deserts occupy about one-fifth of Earth’s land surface, they have been studied to a much lesser extent. All over the world, desert landforms are expanding ev...Deserts are one of the major landforms on the Earth. While deserts occupy about one-fifth of Earth’s land surface, they have been studied to a much lesser extent. All over the world, desert landforms are expanding ever rapidly and more and more human settlements are finding place in desert regions for habitation. Thus, quantifying and monitoring dunes becomes more relevant from a managerial perspective. Analyzing desert areas using satellite imagery is a challenging task due to weak textural differences and nearly homogeneous spectral responses in most parts of the terrain. In this paper, a post-clustering methodology for change detection of desert sand dunes is proposed. Features based on Radon spectrum are used to cluster dunes of various orientations. These clustered boundaries are used to detect if there are any changes occurring in the dune regions. In the experiments, remote sensing data covering various dune regions of the world are observed for possible changes in dune orientations. In all the cases, it is seen that there are no major changes in desert dune orientations since three decades.展开更多
With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due t...With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.展开更多
An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, clo...An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.展开更多
In this paper, we study the topological structure of the singular points of the third order phase locked loop equations with the character of detected phase being g(?) =(1+k)sin?/1+kcos?.
To the problem of the unknown underwater target detection, according to the feature that the underwater target radiated noise contains the stable line spectrum, a weighted method based on the main-to-side lobe ratio ...To the problem of the unknown underwater target detection, according to the feature that the underwater target radiated noise contains the stable line spectrum, a weighted method based on the main-to-side lobe ratio （MSLR） is proposed for broadband beam-forming. This weighted method can be implemented by using the following steps. Firstly, optimize the spatial spectrum of each frequency unit by the second-order cone programming （SOCP）, and obtain the optimized spatial spectrum with lower side lobe. Secondly, construct weighting factors based on the MSLR of the optimized spatial spectrums to from weight factors. Lastly, cumulate the spatial spectrum of each frequency unit via the weight statistical method of this paper. This method can restrain the disturbance of background noise, enhance the output signal-to-noise ratio （SNR）, and overcome the difficulty of traditional four-dimensional display. The theoretical analysis and simulation results both verify that this method can well enhance the spatial spectrum of line spectrum units, restrain the spatial spectrum of background noise units, and improve the performance of the broadband beam-forming.展开更多
In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates ...In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.展开更多
文摘Deserts are one of the major landforms on the Earth. While deserts occupy about one-fifth of Earth’s land surface, they have been studied to a much lesser extent. All over the world, desert landforms are expanding ever rapidly and more and more human settlements are finding place in desert regions for habitation. Thus, quantifying and monitoring dunes becomes more relevant from a managerial perspective. Analyzing desert areas using satellite imagery is a challenging task due to weak textural differences and nearly homogeneous spectral responses in most parts of the terrain. In this paper, a post-clustering methodology for change detection of desert sand dunes is proposed. Features based on Radon spectrum are used to cluster dunes of various orientations. These clustered boundaries are used to detect if there are any changes occurring in the dune regions. In the experiments, remote sensing data covering various dune regions of the world are observed for possible changes in dune orientations. In all the cases, it is seen that there are no major changes in desert dune orientations since three decades.
基金This work was supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-00796Research on Foundational Technologies for 6GAutonomous Security-by-Design toGuarantee Constant Quality of Security).
文摘With the commercialization of 5th-generation mobile communications(5G)networks,a large-scale internet of things(IoT)environment is being built.Security is becoming increasingly crucial in 5G network environments due to the growing risk of various distributed denial of service(DDoS)attacks across vast IoT devices.Recently,research on automated intrusion detection using machine learning(ML)for 5G environments has been actively conducted.However,5G traffic has insufficient data due to privacy protection problems and imbalance problems with significantly fewer attack data.If this data is used to train an ML model,it will likely suffer from generalization errors due to not training enough different features on the attack data.Therefore,this paper aims to study a training method to mitigate the generalization error problem of the ML model that classifies IoT DDoS attacks even under conditions of insufficient and imbalanced 5G traffic.We built a 5G testbed to construct a 5G dataset for training to solve the problem of insufficient data.To solve the imbalance problem,synthetic minority oversampling technique(SMOTE)and generative adversarial network(GAN)-based conditional tabular GAN(CTGAN)of data augmentation were used.The performance of the trained ML models was compared and meaningfully analyzed regarding the generalization error problem.The experimental results showed that CTGAN decreased the accuracy and f1-score compared to the Baseline.Still,regarding the generalization error,the difference between the validation and test results was reduced by at least 1.7 and up to 22.88 times,indicating an improvement in the problem.This result suggests that the ML model training method that utilizes CTGANs to augment attack data for training data in the 5G environment mitigates the generalization error problem.
基金Project(51274250)supported by the National Natural Science Foundation of ChinaProject(2012BAK09B02-05)supported by the National Key Technology R&D Program during the 12th Five-year Plan of China
文摘An integration processing system of three-dimensional laser scanning information visualization in goaf was developed. It is provided with multiple functions, such as laser scanning information management for goaf, cloud data de-noising optimization, construction, display and operation of three-dimensional model, model editing, profile generation, calculation of goaf volume and roof area, Boolean calculation among models and interaction with the third party soft ware. Concerning this system with a concise interface, plentiful data input/output interfaces, it is featured with high integration, simple and convenient operations of applications. According to practice, in addition to being well-adapted, this system is favorably reliable and stable.
文摘In this paper, we study the topological structure of the singular points of the third order phase locked loop equations with the character of detected phase being g(?) =(1+k)sin?/1+kcos?.
基金supported by the National Natural Science Foundation of China(Grant No.61372180)the National Key Scientific Instrument Equipment Development Project of China(Grant No.2013YQ140431)
文摘To the problem of the unknown underwater target detection, according to the feature that the underwater target radiated noise contains the stable line spectrum, a weighted method based on the main-to-side lobe ratio （MSLR） is proposed for broadband beam-forming. This weighted method can be implemented by using the following steps. Firstly, optimize the spatial spectrum of each frequency unit by the second-order cone programming （SOCP）, and obtain the optimized spatial spectrum with lower side lobe. Secondly, construct weighting factors based on the MSLR of the optimized spatial spectrums to from weight factors. Lastly, cumulate the spatial spectrum of each frequency unit via the weight statistical method of this paper. This method can restrain the disturbance of background noise, enhance the output signal-to-noise ratio （SNR）, and overcome the difficulty of traditional four-dimensional display. The theoretical analysis and simulation results both verify that this method can well enhance the spatial spectrum of line spectrum units, restrain the spatial spectrum of background noise units, and improve the performance of the broadband beam-forming.
文摘In this paper,a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults,external disturbances,and parametric uncertainties.The proposed methodology incorporates a residual generation module,including a bank of filters,into an intelligent residual evaluation module.First,residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances.The residual evaluation module is developed based on the suggested series and parallel forms.Further,a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance.A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances,manoeuvres,uncertainties,and noises.The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.
