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Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

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Chinese Journal of Aeronautics 2024年第7期37卷 352-374页

作者： Qian CHEN Hanlin SHENG Jie ZHANG Jiacheng LI College of Energy and Power Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China

Increasing attention has been attracted to the dynamic performance and safety of advanced performance predictive control systems of the next-generation *** latest research demonstrates that Subspace-based Improved Model Predictive Control(SIMPC)can overcome the difficulty in solving the predictive model in MPC/NMPC ***,applying constant design parameters cannot maintain consistent control effects in all ***,the designed system relies too much on sensor-measured data,and thus it is difficult to thoroughly validate the safety of the system because of its high *** means that any potential hardware/software faults will endanger the ***,this paper first presents a novel nonlinear mapping relationship to adaptively tune the tracking weight online with the change of Power Lever Angle(PLA)and real-time relative tracking ***,without introducing additional design parameters,an adaptive Tracking weight-based SIMPC(ATW-SIMPC)controller is designed to improve the control performance in all operating states ***,a Primary/Backup Hybrid Control(PBHC)strategy with the ATW-SIMPC controller as the primary system and the traditional speed(Nf)controller as the backup system is proposed to ensure *** designed affiliated switching controller and the real-time monitor therein can be used to realize reasonable and smooth switching between primary/backup systems,so as to avoid bump *** PBHC system switches to the Nf controller when the ATW-SIMPC controller is wrong because of potential hardware/software faults;otherwise,the ATW-SIMPC controller keeps acting on the *** main results prove that the ATW-SIMPC controller with the optimal nonlinear mapping relationship,compared with the existing SIMPC controller,uplifts the dynamic control performance by 32%and reduces overshoots to an allowable limit,resulting in a better control effect in full *** comparison results consistently i

关键词： Aeroengine Model predictive control Hybrid system adaptive weight Safety

An adaptive weighted Differential Game Guidance Law

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Chinese Journal of Aeronautics 2012年第5期25卷 739-746页

作者： ZHANG Ping FANG Yangwang ZHANG Fengming XIAO Bingsong HU Shiguo ZONG Shuning College of Aeronautics and Astronautics Engineering Air Force Engineering UniversityXi'an 710038China

For intercepting modern high maneuverable targets, a novel adaptive weighted differential game guidance law based on the game theory of mixed strategy is proposed, combining two guidance laws which are derived from the perfect and imperfect in- formation pattern, respectively. The weights vary according to the estimated error of the target＇s acceleration, the guidance law is generated by directly using the estimation of target＇s acceleration when the estimated error is small, and a differential game guidance law with adaptive penalty coefficient is implemented when the estimated error is large. The adaptive penalty coeffi- cients are not constants and they can be adjusted with current target maneuverability. The superior homing performance of the new guidance law is verified by computer simulations.

关键词： differential games guidance laws adaptive weight penalty coefficient information patterns game theory

Infrared image enhancement based on adaptive weighted guided filter

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The Journal of China Universities of Posts and Telecommunications 2022年第2期29卷 73-84页

作者： Lu Ying Huang Shiqi Wang Wenqing Sun Ke School of Automation Xi’an University of Posts and TelecommunicationsXi'an 710121China Xi’an Key Laboratory of Advanced Control and Intelligent Processing Xi’an University of Posts and TelecommunicationsXi'an 710121China

The physical principle of infrared imaging leads to the low contrast of the whole image,the blurring of contour and edge details,and it is also sensitive to *** improve the quality of infrared image and visual effect,an adaptive weighted guided filter(AWGF) for infrared image enhancement algorithm was *** core idea of AWGF algorithm is to propose an adaptive strategy to update the weights of guided filter(GF) parameters,which not only improves the accuracy of regularization parameter estimation in GF theory,but also achieves the purpose of removing infrared image noise and improving its detail contrast.A large number of real infrared images were used to verify AWGF algorithm,and good experimental results were *** with other guided filtering algorithms,the halo phenomenon at the edge of infrared images processed by the AWGF algorithm is significantly avoided,and the evaluation parameter values of information entropy(IE),average gradient(AG),and moment of inertia(MI)are relatively *** shows that the quality of infrared image processed by the AWGF algorithm is better.

关键词： infrared image guided filter(GF) adaptive weight image enhancement regularization parameter

Unsupervised Functional Data Clustering Based on adaptive weights

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Open Journal of Statistics 2023年第2期13卷 212-221页

作者： Yutong Gao Shuang Chen School of Sciences Hebei University of Technology Tianjin China School of Sciences Beijing Institute of Petrochemical Technology Beijing China

In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.

关键词： Functional Data Unsupervised Learning Clustering Functional Principal Component Analysis adaptive weight

Medical Sign Recognition of Lung Nodules Based on Image Retrieval with Semantic Features and Supervised Hashing

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Journal of Computer Science & Technology 2017年第3期32卷 457-469页

作者： Juan-Juan Zhao Ling Pan Peng-Fei Zhao Kiao-Xian Tang College of Computer Science and Technology Taiyuan University of Technology Taiyuan 030024 China Shanxi Provincial People's Hospital Taiyuan 030012 China

Sign recognition is important for identifying benign and malignant nodules. This paper proposes a new sign recognition method based on image retrieval for lung nodules. First, we construct a deep learning framework to extract semantic features that can effectively represent sign information. Second, we translate the high-dimensional image features into compact binary codes with principal component analysis （PCA） and supervised hashing. Third, we retrieve similar lung nodule images with the presented adaptive-weighted similarity calculation method. Finally, we recognize nodule signs from the retrieval results, which can also provide decision support for diagnosis of lung lesions. The proposed method is validated on the publicly available databases： lung image database consortium and image database resource initiative （LIDC-IDRI） and lung computed tomography （CT） imaging signs （LISS）. The experimental results demonstrate our retrieval method substantially improves retrieval performance compared with those using traditional Hamming distance, and the retrieval precision can achieve 87.29%when the length of hash code is 48 bits. The entire recognition rate on the basis of the retrieval results can achieve 93.52%. Moreover, our method is also effective for real-life diagnosis data.

关键词： lung nodule medical sign recognition image retrieval supervised hashing adaptive weight

A parallel complex divider architecture based on DCD iterations for computing complex division in MVDR beamformer

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Journal of Systems Engineering and Electronics 2018年第6期29卷 1124-1135页

作者： KIDAV Jayaraj U SIVA Mangai N M PERUMAL M Pillai Karunya Institute of Technology and Sciences Coimbatore 641114India National Institute of Electronics and Information Technology Calicut 673601India

This paper presents a hardware architecture using mixed pipeline and parallel processing for complex division based on dichotomous coordinate descent(DCD) iterations. The objective of the proposed work is to achieve low-latency and resource optimized complex divider architecture in adaptive weight computation stage of minimum variance distortionless response(MVDR)algorithm. In this work, computation of complex division is modeled as a 2×2 linear equation solution problem and the DCD algorithm allows linear systems of equations to be solved with high degree of computational efficiency. The operations in the existing DCD algorithm are suitably parallel pipelined and the performance is optimized to 2 clock cycles per iteration. To improve the degree of parallelism, a parallel column vector read architecture is *** proposed work is implemented on the field programmable gate array(FPGA) platform and the results are compared with state-of-art literature. It concludes that the proposed architecture is suitable for complex division in adaptive weight computation stage of MVDR beamformer. We demonstrate the performance of the proposed architecture for MVDR beamformer employed in medical ultrasound imaging applications.

关键词： minimum variance distortionless response(MVDR) beamformer adaptive weight dichotomous coordinate descent(DCD) algorithm medical ultrasound imaging

An evaluation and improvement of tropical cyclone prediction in the western North Pacific basin from global ensemble forecasts

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Science China Earth Sciences 2020年第1期63卷 12-26页

作者： Lili LEI Yangjinxi GE Zhemin TAN Xuwei BAO Key Laboratory of Mesoscale Severe Weather/Ministry of Education School of Atmospheric SciencesNanjing UniversityNanjing 210023China Shanghai Typhoon Institute China Meteorological Administration School of Atmospheric SciencesNanjing UniversityNanjing 210023China

Forecasts of tropical cyclones(TCs) of the western North Pacific basin during the period of July to August 2018,especially of Rumbia(2018), Ampil(2018) and Jongdari(2018) that made landfall over Shanghai, have opposed great challenges for numerical models and forecasters. The predictive skill of these TCs are analyzed based on ensemble forecasts of ECMWF and NCEP. Results of the overall performance show that ensemble forecasts of ECMWF generally have higher predictive skill of track and intensity forecasts than those of NCEP. Specifically, ensemble forecasts of ECMWF have higher predictive skill of intensity forecasts for Rumbia(2018) and Ampil(2018) than those of NCEP, and both have low predictive skill of intensity forecasts for Jongdari(2018) at peak intensity. To improve the predictive skill of ensemble forecasts for TCs, a method that estimates adaptive weights for members of an ensemble forecast is proposed. The adaptive weights are estimated based on the fit of ensemble priors and posteriors to observations. The performances of ensemble forecasts of ECMWF and NCEP using the adaptive weights are generally improved for track and intensity forecasts. The advantages of the adaptive weights are more prominent for ensemble forecasts of ECMWF than for those of NCEP.

关键词： Tropical cyclone Ensemble forecast adaptive weight

Research on Reactive Power Optimization of Offshore Wind Farms Based on Improved Particle Swarm Optimization

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Energy Engineering 2023年第9期120卷 2013-2027页

作者： Zhonghao Qian Hanyi Ma Jun Rao Jun Hu Lichengzi Yu Caoyi Feng Yunxu Qiu Kemo Ding State Grid Jiangsu Nantong Electric Power Co. Ltd.Nantong226000China College of Energy and Electrical Engineering Hohai UniversityNanjing211100China

The lack of reactive power in offshore wind farms will affect the voltage stability and power transmission quality of wind *** improve the voltage stability and reactive power economy of wind farms,the improved particle swarmoptimization is used to optimize the reactive power planning in wind ***,the power flow of offshore wind farms is modeled,analyzed and *** improve the global search ability and local optimization ability of particle swarm optimization,the improved particle swarm optimization adopts the adaptive inertia weight and asynchronous learning *** the minimum active power loss of the offshore wind farms as the objective function,the installation location of the reactive power compensation device is compared according to the node voltage amplitude and the actual engineering ***,a reactive power optimizationmodel based on Static Var Compensator is established inMATLAB to consider the optimal compensation capacity,network loss,convergence speed and voltage amplitude enhancement effect of *** the compensation methods in several different locations,the compensation scheme with the best reactive power optimization effect is ***,the optimization results of the standard particle swarm optimization and the improved particle swarm optimization are compared to verify the superiority of the proposed improved algorithm.

关键词： Offshore wind farms improved particle swarm optimization reactive power optimization adaptive weight asynchronous learning factor voltage stability

adaptive Segmentation Model for Images with Intensity Inhomogeneity based on Local Neighborhood Contrast

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Journal of Partial Differential Equations 2021年第3期34卷 224-239页

作者： WANG Yan XIANG Yongjia ZHANG Xuyuan WU Dan College of Biomedical Engineering&Instrument Science Zhejiang UniversityHangzhou 310027China School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331China College of Electronics and Information Engineering Shenzhen UniversityShenzhen 518060China

Segmentation of images with intensity inhomogeneity is a significant task in the field of image processing,especially in medical image processing and *** local region-based models work well on handling intensity inhomogeneity,but they are always sensitive to contour initialization and high *** this paper,we present an adaptive segmentation model for images with intensity inhomogeneity in the form of partial differential ***,a global intensity fitting term and a local intensity fitting term are constructed by employing the global and local image information,***,a tradeoff function is defined to adjust adaptively the weight between two fitting terms,which is based on the neighborhood contrast of image ***,a weighted regularization term related to local entropy is used to ensure the smoothness of evolution ***,a distance regularization term is added for stable level set *** results show that the proposed model without initial contour can segment inhomogeneous images stably and effectively,which thereby avoiding the influence of contour initialization on segmentation ***,the proposed model works better on noise images comparing with two relevant segmentation models.

关键词： Image segmentation partial differential equation adaptive weight local neighborhood constant initialization