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Solving Electromagnetic Inverse Problem Using Adaptive Gradient Descent Algorithm

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷

作者： Liu, Lian Yang, Bo Zhang, Yi Xu, Yixian Peng, Zhong Wang, Feng Zhejiang Univ Sch Earth Sci Key Lab Geosci Big Data & Deep Resource Zhejiang Hangzhou 310027 Peoples R China Zhejiang Wanli Univ Coll Big Data & Software Engn Ningbo 315100 Peoples R China

The nonlinear conjugate gradient (NLCG) algorithm is one of the popular linearized methods used to solve the frequency-domain electromagnetic (EM) geophysical inverse problem. During NLCG iterations, the model gradient guides the searching direction while the line-search algorithm determines the step length of each iteration. Normally, the line search requires solving the corresponding forward problem a few times. Since line search is usually computationally inefficient, we introduce the adaptive gradient descent (AGD) algorithm to accelerate solving the frequency-domain EM inverse problem within the linearized framework. The AGD algorithm is a variant of the classical gradient descent method and has been well-developed and widely used in deep learning. Rather than the time-consuming line search, its core idea is to algebraically manipulate the cumulative gradients and updates of the model from previous iterations to estimate the model parameter variables at the current iteration. For the inversion of magnetotelluric (MT) data, we here designed and implemented a framework using the AGD algorithm combined with the cool-down scheme to tune the regularization parameter. To improve the convergence performance of the AGD algorithm [specifying to Adam and root-mean-square propagation (RMSProp)], we proposed a tolerance strategy which has been tested numerically. To optimize the global learning rate, we carried out some comparative trials in the proposed inversion framework. The inverted results of synthetic and real-world data showed that both the AGD algorithms (Adam and RMSProp) can recover comparable results and save more than a third of CPU time compared with the NLCG algorithm.

关键词： Mathematical models linear programming Inverse problems Deep learning Computational modeling Costs Search problems Adaptive gradient descent (AGD) algorithm cool-down inverse problem magnetotelluric (MT)

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Manifold Regularized Sparse Archetype Analysis Considering Endmember Variability

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2023年 20卷 1-1页

作者： Xu, Mingming Zou, Xin Liu, Shanwei Sheng, Hui Yang, Zhiru China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

Due to the low resolution of hyperspectral images (HSIs), the problem of mixed pixels is common, and hyperspectral unmixing (HU) is a crucial technology to solve the problem of mixed pixels. Among them, nonnegative matrix factorization (NMF) is widely used because it can simultaneously perform endmember and abundance estimations. As a variant of NMF, the archetype analysis (AA) is to find the most representative sample in the dataset, which has strong interpretability compared with NMF. However, traditional AA-based unmixing methods consider only one spectral curve to represent one class, ignoring endmember variability. To solve this problem, a manifold regularized sparse AA unmixing method considering endmember variability is proposed. In this letter, various spectra were included for each class to fully account for variability. In addition, considering the sparsity of abundance, L-2,L-1 regularization is used to impose sparse constraints on abundance, which ensures the sparseness of abundance. Furthermore, a manifold regularization constraint is introduced to use the underlying manifold structure of the data in unmixing, the construction of which is done by superpixel segmentation. The close relationship between the original image and the abundance is preserved. Experimental results on both synthetic and real hyperspectral datasets illustrate that the proposed method is superior to several multiendmember extraction algorithms, AA-based algorithms, and advanced sparse NMF-based algorithms.

关键词： Manifolds Hyperspectral imaging Synthetic data Image segmentation Optimization methods linear programming Image reconstruction Endmember variability hyperspectral unmixing (HU) archetypal analysis L-2,L-1 & nbsp constraint manifold regularization

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Large-scale integration of offshore wind into the Japanese power grid

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SUSTAINABILITY SCIENCE 2021年第2期16卷 429-448页

作者： Komiyama, Ryoichi Fujii, Yasumasa Univ Tokyo Dept Nucl Engn & Management Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

Offshore wind power attracts intensive attention for decarbonizing power supply in Japan, because Japan has 1600 GW of offshore wind potential in contrast with 300 GW of onshore wind. Offshore wind availability in Japan, however, is significantly constrained by seacoast geography where very deep ocean is close to its coastal line, and eventually, nearly 80% of offshore wind resource is found in an ocean depth deeper than 50 m. Therefore, power system planning should consider both the location of available offshore wind resource and the constraint of power grid integration. This paper analyzes the impact of power grid integration of renewable resources including offshore wind power by considering the detailed location of offshore wind resource and the detailed topology of power grid. The study is performed by an optimal power generation mix model, highlighted by detailed spatial resolution derived from 383 nodes and 472 bulk power transmission lines with hourly temporal resolution through a year. The model identifies the optimal integration of power generation from variable renewables, including offshore wind, given those predetermined capacities. The results imply that, together with extensive solar PV integration, total 33 GW of offshore wind, composed of 20 GW of fixed foundation offshore wind and 13 GW of floating offshore wind could contribute to achieve 50% of renewable penetration in the power supply of Japan, and that scale of offshore wind integration provides a technically feasible picture of large-scale renewable integration in the Japanese power sector.

关键词： Offshore wind power Electric power grid Optimization linear programming Variable renewable energy Power system operation

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Conjunctive use modelling and application of Jaya algorithm for optimisation in multi-crop irrigation

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2021年第3期11卷 321-332页

作者： Bhavsar, Pawan N. Patel, Jayantilal N. SV Natl Inst Technol Surat 395007 India

A linear conjunctive use mathematical model is developed for an effective irrigation purpose. Objective of the model is to maximise the net benefit from the available land and available crops without changing the cropping pattern. Conjunctive model was developed and subjected to various constraints. The developed model is being solved by Jaya algorithm. The results obtained by Jaya area validated by widely used optimisation tool LINGO. Optimisation results shows that, to maximise the net benefit all other cultivable area (except sugarcane) has to be reduced by 7%-15%. Overall net benefit is increased to 5%-10% in any given area. From the results it has been concluded that, by utilising both canal water and ground water, canal water can be saved up to 40% which can be used for the other purposes or it can be shared with the neighbouring state.

关键词： irrigation conjunctive use linear programming Jaya algorithm crop water requirement LINGO software package

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A High Density WSN Cluster Positioning Approach

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INTERNATIONAL JOURNAL OF MOBILE COMPUTING AND MULTIMEDIA COMMUNICATIONS 2021年第2期12卷 1-17页

作者： Kang, Qinqing Hunan Int Econ Univ Changsha Hunan Peoples R China

Node self-positioning is one of the supporting technologies for wireless sensor network applications. In this paper, a clustering localization algorithm is proposed for large-scale high-density wireless sensor networks. Firstly, the potential of the node is defined as the basis for the election of the cluster head. The distance between the nodes in the network is calculated indirectly by the relationship between the received signal strength and the communication radius. The topology information in each cluster is saved by the cluster head, and the linear programming method is used in the cluster head to implement the cluster internal relative positioning. Then, from the sink node, the inter-cluster location fusion is gradually implemented, and finally the absolute positioning of the whole network is realized. Compared with the centralized convex programming algorithm, the proposed algorithm has low computational complexity, small traffic, high positioning accuracy, and does not need to know the signal attenuation factor in the environment in advance, and there is anti-noise ability.

关键词： Cluster linear programming Self-Localization Algorithm Wireless Sensor Network (WSN)

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Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony

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PLOS COMPUTATIONAL BIOLOGY 2023年第9期19卷 e1011489-e1011489页

作者： Hashemi, Seirana Razaghi-Moghadam, Zahra Nikoloski, Zoran Univ Potsdam Inst Biochem & Biol Bioinformat Dept Potsdam Germany Max Planck Inst Mol Plant Physiol Syst Biol & Math Modeling Grp Potsdam Germany Penn State Univ Bellefonte PA USA

Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources. Nevertheless, comparative analyses of intracellular flux predictions from pFBA against fluxes estimated from labeling experiments remain scarce. Here, we posited that steady-state flux distributions derived from the principle of maximizing multi-reaction dependencies are of improved accuracy and precision than those resulting from pFBA. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that support the given specific growth rate and exchange fluxes. We showed that the steady-state flux distributions resulting from cbFBA in comparison to pFBA show better agreement with experimentally measured fluxes from 17 Escherichia coli strains and are more precise, due to the smaller space of alternative solutions. We also showed that the same principle holds in eukaryotes by comparing the predictions of pFBA and cbFBA against experimentally derived steady-state flux distributions from 26 knock-out mutants of Saccharomyces cerevisiae. Furthermore, our results showed that intracellular fluxes predicted by cbFBA provide better support for the principle of minimizing metabolic adjustment between mutants and wild types. Together, our findings point that other principles that consider the dynamics and coordination of steady states may govern the distribution of intracellular fluxes. Data on intracellular fluxes in biological systems provid

关键词： Metabolic networks Escherichia coli Saccharomyces cerevisiae Membrane metabolism Metabolic pathways linear programming Metabolites Stoichiometry

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q-rung orthopair fuzzy Aczel-Alsina aggregation operators with multi-criteria decision

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期122卷

作者： Farid, Hafiz Muhammad Athar Riaz, Muhammad Univ Punjab Dept Math Lahore Pakistan

q-rung orthopair fuzzy sets (q-ROFSs) are advantageous for accurately expressing the preferences of decision makers (DMs) due to their membership and non-membership degrees. This paper presents new q-rung orthopair fuzzy aggregation operators (AOs) that are based on Aczel-Alsina (AA) operations. These operators offer several advantages when dealing with real-world problems. The paper introduces new q-ROFS operations, such as the Aczel-Alsina product, sum, exponent, and scalar multiplication. We developed many AOs namely, the "q-rung orthopair fuzzy Aczel-Alsina weighted averaging (q-ROFAAWA) operator", "q-rung orthopair fuzzy Aczel- Alsina ordered weighted averaging (q-ROFAAOWA) operator", "q-rung orthopair fuzzy Aczel-Alsina hybrid averaging (q-ROFAAHA) operator", "q-rung orthopair fuzzy Aczel-Alsina weighted geometric (q-ROFAAWG) operator,"the "q-rung orthopair fuzzy Aczel-Alsina ordered weighted geometric (q-ROFAAOWG) operator", and the "q-rung orthopair fuzzy Aczel-Alsina hybrid geometric (q-ROFAAHG) operator". Various attributes these operators have been defined, including monotonicity, boundary, idempotency and commutativity. The paper demonstrates these properties for the suggested AOs. An algorithm for multi-criteria decision-making has been developed using the proposed aggregation operators with multiple evaluations by DMs and partial weight information under q-ROFSs. To demonstrate the effectiveness of the proposed approach, the paper uses a scenario for selecting the best green supplier. Additionally, the paper provides sensitivity analysis and compares the proposed technique with existing approaches.

关键词： Aczel-Alsina operations Green supplier Aggregation operator Partial weights linear programming

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Control with Positivity Constraint for 2D Continuous-Time Systems in Roesser Model

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JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS 2021年第1期32卷 70-81页

作者： Alfidi, M. Hmamed, A. Tadeo, F. Benzaouia, A. Sidi Mohamed Ben Abdellah Univ Engn Syst & Applicat Lab Natl Sch Appl Sci Fes Morocco LESSI Dept Phys Fac Sci BP 1796 Fes 30000 Morocco Univ Cadi Ayyad LAEPT URAC 28 Fac Sci Semlalia BP 2390 Marrakech Morocco Univ Valladolid Dept Ingn Sistemas & Automat Valladolid 47005 Spain

This paper deals with the stability synthesis for a class of 2D continuous-time systems described by the Roesser model. Conditions for stability and stabilization of positive continuous-time Roesser systems are derived, for which the states take non-negative values whenever the initial conditions are non-negative. As well, the synthesis of state-feedback controllers, including the requirement of positiveness of the controllers, and its extension to uncertain plants are solved in terms of linear programming. In addition, the synthesis problem with non-symmetrical bounds and stabilization are also treated. Numerical examples are included to illustrate the proposed approach.

关键词： 2D continuous-time systems Stabilization Bounded controls Positive systems Positive control Roesser model linear programming

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System LCOE of variable renewable energies: a case study of Japan's decarbonized power sector in 2050

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SUSTAINABILITY SCIENCE 2021年第2期16卷 449-461页

作者： Matsuo, Yuhji Komiyama, Ryoichi Inst Energy Econ Chuo Ku 1-13-1 Kachidoki Tokyo 1040054 Japan Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo 1130033 Japan

Decarbonization of the power sector is an important milestone for the achievement of ambitious GHG reduction targets. Given the intrinsic shortcomings of nuclear power and zero-emission thermal power generation, such as large investment costs and public acceptance, along with the locational limits of dispatchable renewables such as hydro and geothermal, variable renewable energies (VRE) should play an important role to decarbonize the power sector. Very high penetration of VRE, however, would require additional "integration" costs related to grid expansion, power curtailment, and power storage. In this article, focusing on a decarbonized power system in Japan in 2050, we calculated two metrics that capture the non-linear nature of the integration cost related to high VRE penetration: Average system LCOE (levelized cost of electricity) and relative marginal system LCOE. The former metric allocates the integration cost to each power source, which is divided by the adjusted power output, while the latter measures the changes in the total system cost with the substitution of two types of power sources. The results show that both the average and the relative marginal system LCOE of VRE will rise when the share of VRE rises, but the latter will rise much more sharply than the former. This suggests that the anticipated challenges for achieving very high shares of VRE may still exist even if the cost of VRE may decline rapidly in the future. As the relative marginal system LCOE of VRE can be heavily dependent on meteorological conditions, it is essential to use multi-annual data to estimate it. The metric relative marginal system LCOE can be used for the soft-linking of a detailed power sector model to an integrated assessment model, which can contribute to a better quantitative analysis of climate policies.

关键词： System LCOE Integration cost Variable renewable energies linear programming Meteorological data

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On polynomially solvable constrained input selections for fixed and switched linear structured systems

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AUTOMATICA 2023年 158卷

作者： Zhang, Yuan Xia, Yuanqing Liu, Shenyu Sun, Zhongqi Beijing Inst Technol Sch Automat Beijing Peoples R China

This paper investigates two related optimal input selection problems for fixed (non-switched) and switched structured (or structural) systems. More precisely, we consider selecting the minimum cost of inputs from a prior set of inputs, and selecting the inputs of the smallest possible cost with a bound on their cardinality, all to ensure system structural controllability. Those problems have attracted much attention recently;unfortunately, they are NP-hard in general. In this paper, it is found that, if the input structure satisfies certain 'regularizations', which are characterized by the proposed restricted total unimodularity notion, those problems can be solvable in polynomial time via linear programming (LP) relaxations. Particularly, the obtained characterizations depend only on the incidence matrix relating the inputs and the source strongly connected components (SCC) of the system structure, irrespective of how the inputs actuate states within the same SCC. They cover all the currently known polynomially solvable cases (such as the dedicated input case), and contain many new cases unexplored in the past, among which the source-SCC separated input (SSSI) constraint is highlighted. Further, these results are extended to switched systems, and a polynomially solvable condition, namely the joint SSSI constraint, is obtained that does not require each of the subsystems to satisfy the SSSI constraint. We achieve these by first formulating those problems as equivalent integer linear programmings (ILPs), and then proving the total unimodularity of the corresponding constraint matrices. We also study solutions obtained via LP-relaxation and LP-rounding in the general case, resulting in some lower and upper bounds. Several examples are given to illustrate the obtained theoretical results.

关键词： Structural controllability Input selection Switched system linear programming Total unimodularity

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