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Calculation of Gravity and Magnetic Source Boundaries Based on Anisotropy Normalized Variance

Chinese Journal of Geophysics 2013年第4期54卷 560-567页

作者： Heng-Lei ZHANG Tian-You LIU Yu-Shan YANG Institute of Geophysics and Geomatics China University of Geosciences Wuhan 430074 China Key Laboratory of Tectonics and Petroleum Resources (China University of Geosciences) Ministry of Education Wuhan 430074 China

For the calculation of gravity and magnetic source boundaries, the traditional gradient methods are vulnerable to disturbance and the resulting edges may be too cluttered when the field data are affected by noise. Besides, for the weak anomaly, sources tend to interfere with one another and consequently source edges may be impossible to detect. In this paper, we first construct an anisotropic Gauss function based on coordinate rotation, then we propose the formula of calculating anisotropy normalized variance to detect source edges, and clarify its geological significance through theoretical analysis. The numerical experiment shows the method is stable and effective. Applications to both synthetic data and aeromagnetic data in mid Yangtze region indicate that calculation of anisotropy normalized variance can effectively determine the gravity and magnetic source boundaries, especially for weak anomalies. The calculated boundaries bear high-resolution and ample information, and will facilitate the integrated interpretation of data.

关键词： Potential field exploration Anisotropic normalized variance gradient algorithm Gravity and magnetic source boundaries

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Numerical Study on Lorentz Force Detuning Compensation of βg=0.9 650 MHz Elliptic Superconducting Radio Frequency Cavity

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IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第1期34卷 1页

作者： Bhardwaj, Abhimanyu Kumar, Abhay Sumit Singh, Ashutosh Pratap Shukla, Rahul Raja Ramanna Ctr Adv Technol Indore 452013 India Homi Bhabha Natl Inst Mumbai 400094 India

Radiation pressure acting on internal surfaces of the superconducting radio frequency (SRF) cavity in the pulse mode of operation generates periodic deformations of the cavity wall, which changes its resonance frequency. This shift in the resonance frequency, known as dynamic Lorentz force detuning (LFD), causes large reflections of input power from the cavity. The tolerance on detuning of SRF cavity, particularly in high-energy accelerators, is extremely limited due to its high quality factor. Therefore, compensation for dynamic LFD is imperative. In the literature, the implementation of compensating piezoforce pulse and its automation is formulated on the basis of experiments and control algorithms, such as proportional-integral feedback control, disturbance observer-based control, and active noise control. This article presents a compensating piezoforce pulse having a sinusoidal rise computed by gradient algorithm and an exponential decay optimized using a parametric study for limiting the dynamic LFD of beta(g) = 0.9 650 MHz elliptic SRF cavity. The applicability of the gradient algorithm has been demonstrated on a piezoelectric bimorph test structure by performing finite element analysis (FEA) and its experimental verification. Eventually, a compensating piezoforce pulse is applied to control the longitudinal vibration of the cavity for LFD compensation in coupled electromagnetic and structural FEA. The designed compensating piezoforce pulse demonstrates a significant reduction in dynamic LFD.

关键词： ANSYS parametric design language (APDL) gradient algorithm Lorentz force detuning (LFD) piezoelectric actuator superconducting radio frequency (SRF) cavity vibration control

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RBF neural network with optimal selection cluster algorithm and its application

RBF neural network with optimal selection cluster algorithm ...

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4th World Congress on Intelligent Control and Automation

作者： Liu, TN Guan, XZ Liu, ZY Xie, AH Zhang, H Daqing Petr Inst Dept Automat & Control Engn Anda 151400 Heilongjiang Peoples R China

ISBN: (纸本)0780372689

In this paper, it is framed a model of RBF neural network (RBFNN) to solve identification of nonlinear systems. First, it is proposed a kind of optimal selection cluster algorithm. By this algorithm, it is optimally gained the hidden layer node number of RBFNN in terms of input samples. At the same time, it is obtained the initial parameters values of RBF. Then, it is estimated the parameters value of RBF by gradient algorithm with momentum terms, and identified the weights of RBFNN by recursive least square algorithm. With the above two algorithms, it is alternately iterated. By the above hybrid algorithms, it is not only raised identification precision of RBFNN, but also improved generalization property of the net. It is proved the validity of the scheme by its applications.

关键词： RBF neural network optimal selection cluster algorithm identification gradient algorithm recursive least square algorithm

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Stabilization of stochastic systems with hidden Markovian jumps

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Science in China(Series F) 2001年第2期44卷 104-118页

作者：黄民懿郭雷 Institute of Systems Science Chinese Academy of Sciences Beijing China

This paper considers the adaptive control of discrete-time hybrid stochastic systems with unknown randomly jumping parameters described by a finite-state hidden Markov chain. An intuitive yet longstanding conjecture in this area is that such hybrid systems can be adaptively stabilized whenever the rate of transition of the hidden Markov chain is small enough. This paper provides a rigorous positive answer to this conjecture by establishing the global stability of a gradient-algorithm-based adaptive linear-quadratic control.

关键词： stochastic adaptive control Markovian jump parameters gradient algorithm stability linear quadratic control.

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Edge Detection in Potential Fields Using Optimal Auto-Ratios of Vertical gradients

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Chinese Journal of Geophysics 2014年第6期56卷 766-776页

作者： WANG Yan-Guo ZHANG Feng-Xu LIU Cai WANG Zhu-Wen ZHANG Jin College of Nuclear Engineering and Geophysics East China Institute of Technology Nanchang 330013 China College of Geo-Exploration Science and Technology Jilin University Changchun 130026 China

Derivative calculation of potential-fields is widely used in edge detection of geologic bodies. However, the traditional gradient methods are susceptible to noise disturbance, and can lead to instability of results. Besides, the edges of small-sized geologic bodies are difficult to be detected because of superimposed anomalies. In this paper, we give the definition of auto-ratios and present a new method for edge detection by using optimal auto-ratios of vertical derivatives, and elaborate mathematical implication and physical meaning of this method. The model test shows that the method using optimal auto-ratios of vertical gradients can greatly suppress noise disturbance and accurately detect the edges of geologic bodies which cannot be recognized by traditional edge detection methods. In application, we choose gravity data of the Yalujiang Basin where geological conditions are very complex to test the optimal auto-ratio method. The results show that the tectonic boundaries recognized by the optimal auto-ratios of the third-order vertical derivatives correspond well to distribution characteristics of real geologic bodies, which are in agreement with previous work. And the negative anomaly zones delineated by the auto-ratios reflect the work area of the Hunjiang coalfield rather well.

关键词： gradient algorithm Edge detection Noise disturbance Vertical gradient Optimal auto-ratio

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Optimization of ACE mode transition control schedule considering geometric adjustment speed

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INTERNATIONAL JOURNAL OF TURBO & JET-ENGINES 2022年第0期

作者： Song, Keran Jia, Linyuan Chen, Yuchun Tan, Tian Fan, Peiyang Northwestern Polytech Univ Sch Power & Energy Xian 710072 Peoples R China

Aiming to optimize the mode transition control schedule of the adaptive cycle engine (ACE), an optimization method based on a gradient algorithm was proposed. During the mode transition, the compressor surge margin, the total turbine inlet temperature and the combustion chamber fuel-air ratio are not exceeded. The integration of thrust by time was selected as the optimization objective. The geometric parameters were updated via the optimization target derivative. The speed limit of the geometric adjustment mechanism was also considered in the optimization process. The control schedules of two mode transition processes, from triple to double bypass mode and from double to triple-bypass mode were optimized. The optimization method presented in this paper can be applied to various mode transitions and the geometric adjustment speed meets all the constraints.

关键词： ACE geometric adjustment schedule gradient algorithm mode transition

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Measuring the distance from saddle points and driving to locate them over quantum control landscapes

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JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL 2015年第46期48卷 465305-465305页

作者： Sun, Qiuyang Riviello, Gregory Wu, Re-Bing Rabitz, Herschel Princeton Univ Dept Chem Princeton NJ 08544 USA Tsinghua Univ Dept Automat Beijing 100084 Peoples R China TNList Ctr Quantum Informat Sci & Technol Beijing 100084 Peoples R China

Optimal control of quantum phenomena involves the introduction of a cost functional J to characterize the degree of achieving a physical objective by a chosen shaped electromagnetic field. The cost functional dependence upon the control forms a control landscape. Two theoretically important canonical cases are the landscapes associated with seeking to achieve either a physical observable or a unitary transformation. Upon satisfaction of particular assumptions, both landscapes are analytically known to be trap-free, yet possess saddle points at precise suboptimal J values. The presence of saddles on the landscapes can influence the effort needed to find an optimal field. As a foundation to future algorithm development and analyzes, we define metrics that identify the 'distance' from a given saddle based on the sufficient and necessary conditions for the existence of the saddles. algorithms are introduced utilizing the metrics to find a control such that the dynamics arrive at a targeted saddle. The saddle distance metric and saddle-seeking methodology is tested numerically in several model systems.

关键词： quantum optimal control control landscape saddle point gradient algorithm

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Robotic Ad-hoc Networks Connectivity Maintenance based on RF Signal Strength Mapping

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第2期13卷 9-23页

作者： Ayad, Mustafa Voyles, Richard Ayad, Mohamed SUNY Coll Oswego Elect & Comp Engn Oswego NY 13126 USA Purdue Univ Elect & Comp Engn Technol W Lafayette IN 47907 USA

Network connectivity preservation is one of the substantial factors in achieving efficient mobile robot teams' maneuverability. We present a connectivity maintenance method for a robot team's communication. The proposed approach augments the Radio Frequency Mapping Recognition (RFMR) method and the signal strength gradient decent approach for an overall goal to create a Proactive Motion Control algorithm (PMCA). The PMCA algorithm controls and helps strengthen mobile communicating robots' connectivity in the existent Radio Frequency (RF) obstacles. The RFMR method takes advantage of Hidden Markov Models (HMMs) results, which assist in learning electromagnetic environments depending on measurements of RF signal strength. The classification results of HMM lead the robots to resolve whether to continue the current trajectory for avoiding the obstacle shadow or move back to desirable robust Signal Strength (SS) positions. In both cases, the robot will run the gradient approach to determine the signal change trend and drive the robot toward the strong SS direction for maintaining link connectivity. The PMCA, depending on the results of RFMR and gradient approaches, promises to preserve robots' motion control and link connectivity maintenance.

关键词： RF mapping recognition link connectivity gradient algorithm

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Sparse Analyzer Tool for Biomedical Signals

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SENSORS 2020年第9期20卷 2602-2602页

作者： Vujovic, Stefan Draganic, Andjela Zaric, Maja Lakicevic Orovic, Irena Dakovic, Milos Beko, Marko Stankovic, Srdjan Univ Montenegro Fac Elect Engn Podgorica 81000 Montenegro Univ Lusofona Humanidades & Tecnol COPELABS P-1700097 Lisbon Portugal Univ Nova Lisboa Fac Ciencias & Tecnol P-2829517 Monte De Caparica Portugal

The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals' recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.

关键词： biomedical signals compressive sensing concentration measure gradient algorithm OMP SIRA statistical analyzer sparse signal processing TV minimization virtual instrument

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Parameters identification of continuous system based on hybrid genetic algorithm

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26th Chinese Control Conference

作者： Hou Zhixiang Changsha Univ Sci & Technol Changsha 410076 Peoples R China

ISBN: (纸本)9787811240559

A new hybrid genetic algorithm is provided by adding up the advantages of the genetic algorithm and gradient algorithm, as uses the results of gradient algorithm improving the populations of genetic algorithm, and selects the best point as the start point of gradient algorithm next time by comparing the best point of genetic algorithm with the last results of gradient algorithm. Applying the method to estimating the parameters of continuous system the simulation results show it is more quickly than genetic algorithm and owes better anti-noise ability, and improves the defects of genetic algorithm with slower searching ability near a point and it provides a new method for the parameters estimation of continuous system.

关键词： system identification genetic algorithm gradient algorithm parameters estimation optimization

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