In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally a...In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.展开更多
Rational approximation theory occupies a significant place in signal processing and systems theory. This research paper proposes an optimal design of BIBO stable multidimensional Infinite Impulse Response filters with...Rational approximation theory occupies a significant place in signal processing and systems theory. This research paper proposes an optimal design of BIBO stable multidimensional Infinite Impulse Response filters with a realizable (rational) transfer function thanks to the Adamjan, Arov and Krein (AAK) theorem. It is well known that the one dimensional AAK results give the best approximation of a polynomial as a rational function in the Hankel semi norm. We suppose that the Hankel matrix associated to the transfer function has a finite rank.展开更多
This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown cova...This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.展开更多
This paper evaluates the efficacy of two sequential vertical flow filters (VFF), FV1 and FV2, implanted with Typha, in a pilot-scale wastewater treatment system. FV1 comprises three cells (FV1a, FV1b, and FV1c), while...This paper evaluates the efficacy of two sequential vertical flow filters (VFF), FV1 and FV2, implanted with Typha, in a pilot-scale wastewater treatment system. FV1 comprises three cells (FV1a, FV1b, and FV1c), while FV2 consists of two cells (FV2a and FV2b), each designed to reduce various physicochemical and microbiological pollutants from wastewater. Quantitative analyses show significant reductions in electrical conductivity (from 1331 to 1061 μS/cm), biochemical oxygen demand (BOD5 from 655.6 to 2.3 mg/L), chemical oxygen demand (COD from 1240 to 82.2 mg/L), total nitrogen (from 188 to 37.3 mg/L), and phosphates (from 70.9 to 14.6 mg/L). Notably, FV2 outperforms FV1, particularly in decreasing dissolved salts and BOD5 to remarkably low levels. Microbiological assessments reveal a substantial reduction in fecal coliforms, from an initial concentration of 7.5 log CFU/100mL to 3.7 log CFU/100mL, and a complete elimination of helminth eggs, achieving a 100% reduction rate in FV2. The study highlights the impact of design parameters, such as filter material, media depth, and plant species selection, on treatment outcomes. The findings suggest that the judicious choice of these components is critical for optimizing pollutant removal. For instance, different filtration materials show varying efficacies, with silex plus river gravel in FV1c achieving superior pollutant reduction rates. In conclusion, VFFs emerge as a promising solution for wastewater treatment, underscoring the importance of design optimization to enhance system efficiency. Continuous monitoring and adaptation of treatment practices are imperative to ensure water quality, allowing for safe environmental discharge or water reuse. The research advocates for ongoing improvements in wastewater treatment technologies, considering the environmental challenges of the current era. The study concludes with a call for further research to maximize the effectiveness of VFFs in water management.展开更多
The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed wo...The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.展开更多
基金supported in part by the National Key R&D Program of China(2022YFC3401303)the Natural Science Foundation of Jiangsu Province(BK20211528)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(KFCX22_2300)。
文摘In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.
文摘Rational approximation theory occupies a significant place in signal processing and systems theory. This research paper proposes an optimal design of BIBO stable multidimensional Infinite Impulse Response filters with a realizable (rational) transfer function thanks to the Adamjan, Arov and Krein (AAK) theorem. It is well known that the one dimensional AAK results give the best approximation of a polynomial as a rational function in the Hankel semi norm. We suppose that the Hankel matrix associated to the transfer function has a finite rank.
文摘This paper proposes linear and nonlinear filters for a non-Gaussian dynamic system with an unknown nominal covariance of the output noise.The challenge of designing a suitable filter in the presence of an unknown covariance matrix is addressed by focusing on the output data set of the system.Considering that data generated from a Gaussian distribution exhibit ellipsoidal scattering,we first propose the weighted sum of norms(SON)clustering method that prioritizes nearby points,reduces distant point influence,and lowers computational cost.Then,by introducing the weighted maximum likelihood,we propose a semi-definite program(SDP)to detect outliers and reduce their impacts on each cluster.Detecting these weights paves the way to obtain an appropriate covariance of the output noise.Next,two filtering approaches are presented:a cluster-based robust linear filter using the maximum a posterior(MAP)estimation and a clusterbased robust nonlinear filter assuming that output noise distribution stems from some Gaussian noise resources according to the ellipsoidal clusters.At last,simulation results demonstrate the effectiveness of our proposed filtering approaches.
文摘This paper evaluates the efficacy of two sequential vertical flow filters (VFF), FV1 and FV2, implanted with Typha, in a pilot-scale wastewater treatment system. FV1 comprises three cells (FV1a, FV1b, and FV1c), while FV2 consists of two cells (FV2a and FV2b), each designed to reduce various physicochemical and microbiological pollutants from wastewater. Quantitative analyses show significant reductions in electrical conductivity (from 1331 to 1061 μS/cm), biochemical oxygen demand (BOD5 from 655.6 to 2.3 mg/L), chemical oxygen demand (COD from 1240 to 82.2 mg/L), total nitrogen (from 188 to 37.3 mg/L), and phosphates (from 70.9 to 14.6 mg/L). Notably, FV2 outperforms FV1, particularly in decreasing dissolved salts and BOD5 to remarkably low levels. Microbiological assessments reveal a substantial reduction in fecal coliforms, from an initial concentration of 7.5 log CFU/100mL to 3.7 log CFU/100mL, and a complete elimination of helminth eggs, achieving a 100% reduction rate in FV2. The study highlights the impact of design parameters, such as filter material, media depth, and plant species selection, on treatment outcomes. The findings suggest that the judicious choice of these components is critical for optimizing pollutant removal. For instance, different filtration materials show varying efficacies, with silex plus river gravel in FV1c achieving superior pollutant reduction rates. In conclusion, VFFs emerge as a promising solution for wastewater treatment, underscoring the importance of design optimization to enhance system efficiency. Continuous monitoring and adaptation of treatment practices are imperative to ensure water quality, allowing for safe environmental discharge or water reuse. The research advocates for ongoing improvements in wastewater treatment technologies, considering the environmental challenges of the current era. The study concludes with a call for further research to maximize the effectiveness of VFFs in water management.
文摘The Internet of Things(IoT)is a growing technology that allows the sharing of data with other devices across wireless networks.Specifically,IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks.In this framework,a Covariance Linear Learning Embedding Selection(CL2ES)methodology is used at first to extract the features highly associated with the IoT intrusions.Then,the Kernel Distributed Bayes Classifier(KDBC)is created to forecast attacks based on the probability distribution value precisely.In addition,a unique Mongolian Gazellas Optimization(MGO)algorithm is used to optimize the weight value for the learning of the classifier.The effectiveness of the proposed CL2ES-KDBC framework has been assessed using several IoT cyber-attack datasets,The obtained results are then compared with current classification methods regarding accuracy(97%),precision(96.5%),and other factors.Computational analysis of the CL2ES-KDBC system on IoT intrusion datasets is performed,which provides valuable insight into its performance,efficiency,and suitability for securing IoT networks.
