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Ascent trajectory planning on-board based on direct optimisation method

INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND control 2013年第4期19卷 388-395页

作者： Lu, Xuefang Wang, Yongji Huazhong Univ Sci & Technol Dept Control Sci & Engn Key Lab Minist Educ Image Proc & Intelligent Control Wuhan Hubei Peoples R China

In this paper, an on-board trajectory planning algorithm is proposed for atmospheric ascent. To deal with the impact of disturbance, the on-board trajectory planning algorithm updates the reference trajectory by solving an optimal control problem on-board. Considering the strong non-linear aerodynamic, the optimal control problem is transformed into non-linear programming problem by trajectory discretisation. The direct optimisation method is implemented for this non-linear programming problem. Due to the small amount of discrete nodes and the initial guess solution which is close to the optimisation solution, the direct optimisation method is fast enough to generate a new reference trajectory in every guidance cycle. With different cases of the aerodynamic coefficient bias, numerical simulation for the generic hypersonic vehicle model and scramjet engine is done. The results show the accuracy and the effectiveness of the on-board trajectory planning algorithm.

关键词： trajectory planning ascent direct optimisation non-linear programming problem

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A Survey on Negative Transfer

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 305-329页

作者： Wen Zhang Lingfei Deng Lei Zhang Dongrui Wu the Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China IEEE the School of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

Transfer learning(TL)utilizes data or knowledge from one or more source domains to facilitate learning in a target *** is particularly useful when the target domain has very few or no labeled data,due to annotation expense,privacy concerns,***,the effectiveness of TL is not always *** transfer(NT),i.e.,leveraging source domain data/knowledge undesirably reduces learning performance in the target domain,and has been a long-standing and challenging problem in *** approaches have been proposed in the literature to address this ***,there does not exist a systematic *** paper fills this gap,by first introducing the definition of NT and its causes,and reviewing over fifty representative approaches for overcoming NT,which fall into three categories:domain similarity estimation,safe transfer,and NT *** areas,including computer vision,bioinformatics,natural language processing,recommender systems,and robotics,that use NT mitigation strategies to facilitate positive transfers,are also ***,we give guidelines on NT task construction and baseline algorithms,benchmark existing TL and NT mitigation approaches on three NT-specific datasets,and point out challenges and future research *** ensure reproducibility,our code is publicized at https://***/chamwen/NT-Benchmark.

关键词： Domain adaptation domain similarity negative transfer positive transfer transfer learning

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Simultaneous Destriping and Denoising for Remote Sensing images With Unidirectional Total Variation and Sparse Representation

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2014年第6期11卷 1051-1055页

作者： Chang, Yi Yan, Luxin Fang, Houzhang Liu, Hai Huazhong Univ Sci & Technol Sch Automat Key Lab Minist Educ Image Proc & Intelligent Control Wuhan 430074 Peoples R China

Remote sensing images destriping and denoising are both classical problems, which have attracted major research efforts separately. This letter shows that the two problems can be successfully solved together within a unified variational framework. To do this, we proposed a joint destriping and denoising method by integrating the unidirectional total variation and sparse representation regularizations. Experimental results on simulated and real data in terms of qualitative and quantitative assessments show significant improvements over conventional methods.

关键词： Denoising destriping remote sensing image sparse representation unidirectional total variation (UTV)

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Blind restoration of real turbulence-degraded image with complicated backgrounds using anisotropic regularization

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OPTICS COMMUNICATIONS 2012年第24期285卷 4977-4986页

作者： Hong, Hanyu Li, Liangcheng Zhang, Tianxu Wuhan Inst Technol Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & AI State Key Lab Image Proc & Intelligent Control Wuhan 430074 Peoples R China Minist Publ Secur Res Inst 3 Key Lab Informat Network Secur Shanghai 200031 Peoples R China

This paper proposes a novel blind image restoration method based on estimating the point-spread functions by using two real turbulence-degraded images as input. The non-negative constraint and the spatial correlation are transformed mathematically into the penalty terms and added to the objective function. An anisotropic and nonlinear regularization function is proposed to adequately punish the differences of the point spread functions (PSFs) in the process of optimization estimation. Some definitions of weighted second-order differences are given and a fast method to construct the matrix of second-order weighted gradient operator is derived. The PSF values can be quickly estimated. With the estimated PSFs, the true images can be recovered by non-blind restoration methods. Experiment results for the restoration of real turbulence-degraded images with complicated backgrounds support the effectiveness of this proposed method. (C) 2012 Elsevier B.V. All rights reserved.

关键词： image restoration Optimization estimation Anisotropic regularization

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Local feature representation based on linear filtering with feature pooling and divisive normalization for remote sensing image classification

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JOURNAL OF APPLIED REMOTE SENSING 2017年第1期11卷 016017-1-016017-24页

作者： Wan, Lihong Liu, Na Guo, Yiyou Huo, Hong Fang, Tao Shanghai Jiao Tong Univ Inst Image Processing & Pattern Recogn Dept Automat Shanghai Peoples R China Minist Educ Key Lab Syst Control & Informat Processing Shanghai Peoples R China

We propose a local feature representation based on two types of linear filtering, feature pooling, and nonlinear divisive normalization for remote sensing image classification. First, images are decomposed using a bank of log-Gabor and Gaussian derivative filters to obtain filtering responses that are robust to changes in various lighting conditions. Second, the filtering responses computed using the same filter at nearby locations are pooled together to enhance position invariance and compact representation. Third, divisive normalization with channelwise strategy, in which each pooled feature is divided by a common factor plus the sum of the neighboring features to reduce dependencies among nearby locations, is introduced to extract divisive normalization features (DNFs). Power-law transformation and principal component analysis are applied to make DNF significantly distinguishable, followed by feature fusion to enhance local description. Finally, feature encoding is used to aggregate DNFs into a global representation. Experiments on 21-class land use and 19-class satellite scene datasets demonstrate the effectiveness of the channel-wise divisive normalization compared with standard normalization across channels and the fusion of the two types of linear filtering in improving classification accuracy. The experiments also illustrate that the proposed method is competitive with state-of-the-art approaches. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： feature pooling feature encoding linear filtering nonlinear divisive normalization remote sensing image

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Spiking neural P systems for arithmetic operations

Spiking neural P systems for arithmetic operations

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6th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2011

作者： Zeng, Xiangxiang Song, Tao Pan, Linqiang Zhang, Xingyi Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9780769545141

Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes. © 2011 IEEE.

关键词： Encoding (symbols)

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Detection of driver metabolites in the human liver metabolic network using structural controllability analysis

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BMC SYSTEMS BIOLOGY 2014年第1期8卷 51-51页

作者： Liu, Xueming Pan, Linqiang Huazhong Univ Sci & Technol Key Lab Image Informat Proc & Intelligent Control Sch Automat Wuhan 430074 Peoples R China

Background: Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results: We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential;the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion: There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions;the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism.

关键词： Human liver metabolic network controllability Driver metabolite

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Transmission-Constrained Consensus of Multiagent Networks

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IEEE Transactions on control of Network Systems 2023年第3期10卷 1484-1495页

作者： Wang, Xiaotian Su, Housheng Huazhong University of Science and Technology School of Artificial Intelligence and Automation Hubei430074 China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan430074 China

This article studies the consensus problem for multiagent systems with transmission constraints. A novel model of multiagent systems is proposed where the information transmissions between agents are disturbed by irregular distortions or interferences (named transmission constraint functions), and this model is universal which can be applied in many cases, such as interval consensus and discarded consensus. In the transmission-constrained consensus problem, we obtain the necessary and sufficient condition that agents can converge to state consensus. Furthermore, a more general case is studied in which the system reaches an equilibrium. Based on some techniques of stability theory, the existence, uniqueness, and stability of the system equilibrium point can be proven, which means the system can reach an asymptotically stable equilibrium. Moreover, the state values of the equilibrium are only decided by the network structure and transmission constraint functions but not the agents' initial states. Finally, numerical simulations are presented to illustrate the proposed theorems and corollaries. © 2014 IEEE.

关键词： Multi agent systems

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Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

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Memetic Computing 2010年第1期2卷 47-67页

作者： Gong, Maoguo Liu, Chao Jiao, Licheng Cheng, Gang Key Lab of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University 710071 Xi'an China

Lamarckian learning has been introduced into evolutionary computation as local search mechanism. The relevant research topic, memetic computation, has received significant amount of interests. In this study, a novel Lamarckian learning strategy is designed for improving the Nondominated Neighbor Immune Algorithm, a novel hybrid multi-objective optimization algorithm, Multi-objective Lamarckian Immune Algorithm (MLIA), is proposed. The Lamarckian learning performs a greedy search which proceeds towards the goal along the direction obtained by Tchebycheff approach and generates the improved progenies or improved decision vectors, so single individual will be optimized locally and the newcomers yield an enhanced exploitation around the nondominated individuals in less-crowded regions of the current trade-off front. Simulation results based on twelve benchmark problems show that MLIA outperforms the original immune algorithm and NSGA-II in approximating Pareto-optimal front in most of the test problems. When compared with the state of the art algorithm MOEA/D, MLIA shows better performance in terms of the coverage of two sets metric, although it is laggard in the hypervolume metric. © 2009 Springer-Verlag.

关键词： Artificial immune system Lamarckian learning Memetic algorithm Multi-objective optimization

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Bootstrapping Autonomous Lane Changes with Self-supervised Augmented Runs 17th

Bootstrapping Autonomous Lane Changes with Self-supervised ...

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17th European Conference on Computer Vision, ECCV 2022

作者： Xiang, Xiang Key Lab of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031250682

In this paper, we want to strengthen an autonomous vehicle’s lane-change ability with limited lane changes performed by the autonomous system. In other words, our task is bootstrapping the predictability of lane-change feasibility for the autonomous vehicle. Unfortunately, autonomous lane changes happen much less frequently in autonomous runs than in manual-driving runs. Augmented runs serve well in terms of data augmentation: the number of samples generated from augmented runs in a single one is comparable with that of samples retrieved from real runs in a month. In this paper, we formulate the Lane-Change Feasibility Prediction problem and also propose a data-driven learning approach to solve it. Experimental results are also presented to show the effectiveness of learned lane-change patterns for the decision making. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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