To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary stat...To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.展开更多
The release of AlphaFold2 has sparked a rapid expansion in protein model databases.Efficient protein structure retrieval is crucial for the analysis of structure models,while measuring the similarity between structure...The release of AlphaFold2 has sparked a rapid expansion in protein model databases.Efficient protein structure retrieval is crucial for the analysis of structure models,while measuring the similarity between structures is the key challenge in structural retrieval.Although existing structure alignment algorithms can address this challenge,they are often time-consuming.Currently,the state-of-the-art approach involves converting protein structures into three-dimensional(3D)Zernike descriptors and assessing similarity using Euclidean distance.However,the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based,thus limiting their application in studying custom datasets.To overcome this limitation,we developed FP-Zernike,a user-friendly toolkit for computing different types of Zernike descriptors based on feature points.Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets.FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets.In addition,we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank(PDB)dataset to facilitate the local deployment of this tool for interested readers.Our demonstration contained 590,685 structures,and at this scale,our system required only 4-9 s to complete a retrieval.The experiments confirmed that it achieved the state-of-the-art accuracy level.FP-Zernike is an open-source toolkit,with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1,as well as through a webserver at http://www.structbioinfo.cn/.展开更多
基金supported by the National Natural Science Foundation of China (60874054)
文摘To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.
基金supported by the National Key R&D Program of China(Grant Nos.2021YFF0704300 and 2020YFA0712400)the National Natural Science Foundation of China(Grant Nos.62072280,61771009,61932018,62072441,32241027,and T2225007)+1 种基金the open project of BGI-Shenzhen,Shenzhen 518000,China(Grant No.BGIRSZ20220005)the Natural Science Foundation of Ningxia Province,China(Grant No.2023AAC05036).
文摘The release of AlphaFold2 has sparked a rapid expansion in protein model databases.Efficient protein structure retrieval is crucial for the analysis of structure models,while measuring the similarity between structures is the key challenge in structural retrieval.Although existing structure alignment algorithms can address this challenge,they are often time-consuming.Currently,the state-of-the-art approach involves converting protein structures into three-dimensional(3D)Zernike descriptors and assessing similarity using Euclidean distance.However,the methods for computing 3D Zernike descriptors mainly rely on structural surfaces and are predominantly web-based,thus limiting their application in studying custom datasets.To overcome this limitation,we developed FP-Zernike,a user-friendly toolkit for computing different types of Zernike descriptors based on feature points.Users simply need to enter a single line of command to calculate the Zernike descriptors of all structures in customized datasets.FP-Zernike outperforms the leading method in terms of retrieval accuracy and binary classification accuracy across diverse benchmark datasets.In addition,we showed the application of FP-Zernike in the construction of the descriptor database and the protocol used for the Protein Data Bank(PDB)dataset to facilitate the local deployment of this tool for interested readers.Our demonstration contained 590,685 structures,and at this scale,our system required only 4-9 s to complete a retrieval.The experiments confirmed that it achieved the state-of-the-art accuracy level.FP-Zernike is an open-source toolkit,with the source code and related data accessible at https://ngdc.cncb.ac.cn/biocode/tools/BT007365/releases/0.1,as well as through a webserver at http://www.structbioinfo.cn/.
