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Full Waveform Inversion Method for Roadways Based on Wave Velocity Structure Correction and Regularization Constraints

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Li, Shenglin Zhang, Pingsong Hu, Xiongwu Jin, Xueliang Wu, Rongxin Chen, Huiting Anhui Univ Sci & Technol State Key Lab Min Response & Disaster Prevent & Co Huainan 232001 Anhui Peoples R China Anhui Univ Sci & Technol Sch Earth & Environm Huainan 232001 Anhui Peoples R China Huaibei Min Grp Co Ltd Huaibei 235000 Anhui Peoples R China

Concealed geological formations that induce disasters frequently result in incidents, such as water and gas outburst, during coal mine roadway excavation, thereby underscoring the critical necessity for precise advanced exploration of geological structures and their spatial positions to ensure safe excavation practices. Full waveform inversion (FWI) presents several advantages, including high-resolution imaging of complex geological structures and the effective inversion of essential physical parameters. However, the unique observational conditions, such as the linear arrangement of vertically excavated working faces in the roadway whole space and the implementation of single-offset detection, significantly exacerbate the challenge of non-uniqueness in the inversion process. To mitigate the problem of cycle skipping in the FWI of seismic advanced detection within roadways and to enhance inversion accuracy, a study was undertaken to investigate the impact characteristics of FWI under these unique observational conditions present. Initiating from the model structure and incorporating a time-domain multiscale inversion strategy, a novel approach was proposed for constructing a single-scale initial model based on wave velocity structure correction, thereby yielding a more accurate initial model for subsequent scales of inversion. Furthermore, a convolutional wavelet-independent inversion method, along with a total variation regularization approach, was introduced to effectively tackle the challenges of wavelet estimation in actual data inversion, as well as to more effectively mitigate the nonlinearity and ill-posedness inherent in the inversion process. Research findings indicate that, in comparison to conventional elastic wave FWI methods, the newly developed roadway FWI methodology can enhance inversion accuracy by approximately 20% in roadway scenarios, thereby providing high-precision imaging results.

关键词： linear programming Accuracy Mathematical models Excavation Coal mining Finite difference methods Data models Convolution Geology Time-domain analysis Coal mine roadway full waveform inversion (FWI) regularization seismic advanced detection wave velocity structure correction wavelet-independent

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Equilibrium Optimizer for Supply Chain Design Under Demand Uncertainty

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IEEE ACCESS 2025年 13卷 42285-42295页

作者： Li, Xiaofang Hu, Pei Zhu, Jiulong Nanyang Inst Technol FanLi Business Sch Nanyang 473004 Henan Peoples R China Nanyang Inst Technol Sch Comp & Software Nanyang 473004 Henan Peoples R China

Supply chain design (SCD) is a complex optimization challenge that involves coordinating various elements of a supply chain to ensure efficient production, distribution, and fulfillment of customer demands. This paper proposes an improved equilibrium optimizer (IEO) algorithm to develop a supply chain network. The first novelty lies in considering the uncertainty of customer demands, the upper and lower product limits of manufacturers, and product discounts to minimize the total economic cost. The second novelty concerns the improvements to the EO algorithm in the equilibrium pool, control parameters, and position correction. Position correction ensures that solutions meet the various constraints of SCD, and improves the feasibility of the algorithm. For small-, medium-, and large-scale test cases, the proposed algorithm has been observed to outperform the original EO algorithm and four well-known algorithms, the imperialist competitive algorithm (ICA), a hybrid algorithm of grey wolf optimizer and particle swarm optimization (GWOPSO), the whale optimization algorithm (WOA), and the teaching-learning based optimization algorithm (TLBO), in terms of optimal solutions and operational efficiency. IEO demonstrates outstanding performance in solving SCD problems.

关键词： Supply chains Costs Uncertainty Adaptation models Transportation Sustainable development Routing programming linear programming Genetic algorithms Supply chain design equilibrium optimizer demand uncertainty

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Nested optimal energy scheduling and power allocation for zero-emission high-speed passenger vessels

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OCEAN ENGINEERING 2025年 322卷

作者： Najjaran, Samieh Skjetne, Roger Norwegian Univ Sci & Technol NTNU Dept Marine Technol Jonsvannsveien 82 N-7050 Trondheim Norway

The transition towards sustainable transportation has sparked growing interest in developing zero-emission high-speed passenger vessels (HSPVs), which necessitates sophisticated energy management systems (EMS). This study presents a novel nested optimization method proposed for zero-emission power systems onboard HSPVs. The method computes the optimal scheduling of energy and consistent allocation of power fora given load profile. It is benchmarked against a global optimal solution obtained using a linear programming (LP) method, referred to as direct power allocation, to evaluate its optimality and computational efficiency. Two potential zero-emission hybrid power systems-a non-plugin hybrid system and a plugin hybrid system, both powered by a fuel cell (FC) and a battery energy storage system (BESS)-are assessed using this method to demonstrate its applicability and to evaluate their operational performance. Additionally, the energy consumption of a BESS-only system and a FC-only system, is calculated for comparison. The results highlight the potential of the nested optimization method for achieving a near-optimal utilization of the power plant in a zero-emission HSPV with high computational efficiency.

关键词： High-speed passenger vessel Zero-emission power systems Energy scheduling Power allocation linear programming

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A Framework for Energy Efficiency Optimization in IRS-Aided Hybrid MU-MIMO Systems

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2025年第3期43卷 883-898页

作者： Ju, Xin Liu, Heng Gong, Shiqi Xing, Chengwen Zhao, Nan Niyato, Dusit Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing 100081 Peoples R China Dalian Univ Technol Sch Informat & Commun Engn Dalian 116024 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci Singapore Singapore

Energy efficiency (EE) optimization has attracted significant research attention for implementing green communications. With cost-effective and low-power advantages, intelligent reflecting surface (IRS) and hybrid analog-digital transceiver have recently emerged as two promising technologies of next-generation green wireless systems. In this paper, we propose a comprehensive framework for EE optimization in four types of IRS-aided hybrid analog-digital multiuser multiple-input multiple-output communication systems, including the uplink (UL) systems under the sum power and box eigenvalue constraints as well as the per-radio-frequency chain power constraints (PRPCs), and the downlink (DL) systems under the sum power constraint and the PRPCs. This framework proposes a unified design methodology to these four considered systems by separating the optimization of analog and digital matrix variables. Specifically, for the UL EE maximization problems, we firstly propose a channel alignment based algorithm to separately optimize the analog precoders at users, the analog combiner at the base station and the IRS reflecting matrix, whose computational complexity is significantly reduced as compared with the traditional alternating optimization algorithm. Then, by introducing the auxiliary variables and exploiting the Karush-Kuhn-Tucker conditions based algorithm, the optimal digital precoders at users are obtained in closed forms. Furthermore, the intractable DL EE optimization can be equivalently transformed into its virtual UL counterpart using the DL-UL duality, leading to the general applicability of the proposed framework. Extensive simulations reveal that the proposed algorithm attains the almost identical EE performance to the traditional benchmarks with a lower computational complexity.

关键词： Optimization Radio frequency Analog-digital conversion Transceivers Ink Hybrid power systems linear programming Approximation algorithms Massive MIMO Eigenvalues and eigenfunctions Energy efficiency duality hybrid analog-digital system intelligent reflecting surface multiuser multiple-input multiple-output

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Complex Path Planning for Manipulators Based on an Improved NSGA-III Algorithm With Hopfield Network

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IEEE ACCESS 2025年 13卷 60186-60199页

作者： Sun, Xiaoxiang Wang, Jue Wang, Wei Zhejiang Sci Tech Univ Sch Mech Engn Hangzhou 310018 Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China

In various production applications across industries such as manufacturing and agriculture, manipulators often face complex trajectories composed of numerous locally discrete paths during movement. To enhance planning efficiency and convergence accuracy, this paper proposes an improved NSGA-III multi-objective optimization algorithm based on Hopfield neural network. First, we construct an optimization objective function considering three dimensions: the length of the manipulator's motion path, motion time, and joint impact. We introduce a Hopfield neural network to replace the initial random sampling of the NSGA-III algorithm, allowing for the rapid generation of an initial optimized population. Based on non-dominance relationships, we use reference points for population crossover, mutation, and selection, thereby accelerating convergence to the optimal solution. Additionally, we employ seventh-degree polynomial smooth interpolation to improve the geometric properties of the trajectory. The main contributions include: By utilizing the energy gradient of the Hopfield neural network, we rapidly reduce the random sampling space formed by the multi-objective functions along the gradient direction towards the suboptimal solution set. This approach effectively eliminates a significant number of inferior solutions during the iterations of NSGA-III, leading to a marked improvement in the efficiency of generating the Pareto front. Specifically, the optimization efficiency is enhanced by 45.86%, the length of the robot's motion path is reduced by 77.9%, motion efficiency is increased by 51.89%, and joint motion stability is improved by 69.45%. The feasibility of the algorithm has been validated through simulations and experiments in complex three-dimensional motion scenarios.

关键词： Manipulators Optimization Hopfield neural networks Robots Trajectory linear programming Genetic algorithms Sorting Service robots Robot kinematics Manipulator trajectory planning multi-objectives optimization NSGA-III Hopfield

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Challenges and opportunities in generation expansion for 100% renewable energy systems: an optimization approach

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OPTIMIZATION AND ENGINEERING 2025年第2期26卷 827-868页

作者： Koltsaklis, Nikolaos E. Knapek, Jaroslav Czech Tech Univ Fac Elect Engn Tech 2 Prague 16627 Czech Republic

Generation expansion planning is defined as the problem determining the optimal type of energy technologies to be installed, time, and construction location. This problem becomes even more complicated within the context of electricity markets since market conditions have to be taken into consideration, including their volatilities and shocks. This work addresses the problem of optimal generation expansion planning, taking into account the market operation through the introduction of unit commitment constraints. In addition, it considers the penetration of distributed energy resources into the system, including energy storage systems and demand response programs. The overall methodological framework is based on linear programming techniques and has been tested in an illustrative power system under various assumptions regarding CO2 emissions limits as well as CO2 and natural gas fuel prices. The results highlight that renewable power units are installed in all cases, while non-renewable power units are installed only if they are low-carbon (biomass and nuclear) or they are equipped with a CCS technology (hard coal and natural gas). In addition, there is a positive correlation with the installation of energy storage systems;namely, the more the capacity of the installed RES units, the more the capacity of the installed energy storage systems. The increase in the price of natural gas fuel is able to lead to power mixes with higher CO2 intensity, if it is combined with a low CO2 emissions price. Energy storage systems, accompanied by demand response programs to some extent, play a decisive role in both energy and reserves balance in the zero-emissions power mix. System operators, regulatory authorities, and potential investors can utilize the developed optimization framework to quantify the roadmap and the long-term dynamics of the studied power system to optimize the investment strategy of their resources and portfolios.

关键词： Optimization linear programming Generation expansion planning Energy and reserves Energy storage Demand response

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Traceability Technology Adoption in Supply Chain Networks

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MANAGEMENT SCIENCE 2025年第1期71卷 83-102页

作者： Blaettchen, Philippe Calmon, Andre P. Hall, Georgina Bayes Business Sch formerly Cass London EC1Y 8TZ England Georgia Inst Technol Scheller Coll Business Atlanta GA 30308 USA INSEAD Decis Sci F-77305 Fontainebleau France

Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, and verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms-or seed set-to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product's supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to solve and even approximate within a polylogarithmic factor. Nevertheless, we provide a novel linear programming-based algorithm to identify the least- costly seed set. The algorithm is fixed-parameter tractable in the supply chain network's treewidth, which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily computable bounds on the cost of selecting an optimal seed set. We leverage our toolbox to conduct large-scale numerical experiments that provide insights into how the supply chain network structure influences diffusion. These insights can help managers optimize their technology diffusion strategy.

关键词： supply chain traceability sustainability technology adoption network diffusion computational complexity fixed-parameter tractability treewidth linear programming

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Equalizer Optimization Method Based on Local Multi-Constraint Modeling-Bayesian Optimization With Region Partitioning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Li, Xiang-Ru Zhang, Peng Wang, Da-Wei Liu, Jun Sun, Lingling Zhao, Wen-Sheng Hangzhou Dianzi Univ Sch Elect & Informat Sch IC Sci & Engn Innovat Ctr Elect Design Automat Technol Hangzhou 310018 Peoples R China

As an important technology in high-speed systems, equalizer (EQ) is used to mitigate inter-symbol interference (ISI) caused by inconsistent attenuation of high and low frequencies. The difficulty of signal integrity improvement increases the complexity of EQ design, making the existing algorithms inefficient in high-dimensional searching and constraint processing. In this article, a local multi-constraint modeling-Bayesian optimization (BO) with region partitioning is proposed, aiming to provide a general optimization solution for high-dimensional multi-constraint EQs and improve convergence accuracy and efficiency. The constraint filtering mechanism is used to exclude areas that violate simulation-independent constraints. Local modeling and region partitioning techniques complement each other, taking into account both the local accuracy of the model and the global search performance of the algorithm. The multi-constraint modeling strategy allows simulation-dependent constraints to be pre-judged through the surrogate model, overcoming the shortcomings of the traditional solution of adding the penalty term to the target value, which makes it difficult to balance the weights and can only judge the constraints after simulation, thereby reducing the waste of computing resources caused by simulating data that violates the constraints. The proposed algorithm is applied to EQ optimization in a 16 Gbps high-bandwidth memory channel and a 64 Gbps differential peripheral component interconnect express channel, respectively. The algorithm is developed based on PyTorch, and the eye diagrams are obtained using Keysight ADS software. Two applications are conducted on computer with Intel Core i5-13500 processor and 32 GB RAM. By utilizing the region partitioning and constraint filtering techniques, the actual number of simulations in the optimization can be significantly reduced. The experimental results demonstrate that the proposed algorithm has significant shorter computing tim

关键词： Optimization Data models Partitioning algorithms Integrated circuit modeling Genetic algorithms Accuracy Predictive models Analytical models Software algorithms linear programming 3-D integration equalizer signal integrity eye diagram Bayesian optimization region partitioning multi-constraint modeling

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An Efficient Reconfigurable Battery Network Based on the Asynchronous Advantage Actor-Critic Paradigm

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IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION 2025年第1期11卷 1479-1487页

作者： Yang, Feng Meng, Jinhao Ci, Marvin Lin, Ni Gao, Fei Yinfei Technol Nanjing 210000 Peoples R China Xi An Jiao Tong Univ Coll Elect Engn Xian 710049 Peoples R China Elkhorn South High Sch Omaha NE 68130 USA Beijing Inst Technol Sch Mech & Vehicular Engn Beijing 100081 Peoples R China Univ Technol Belfort Montbeliard Sch Energy & Comp Sci F-90000 Belfort France

Although the battery systems continue to grow in electric vehicles (EVs), smart grids, and backup power systems due to their capability to supply power and energy, the current battery systems essentially are fixed which limits efficient energy usage. Recently, by employing the digital battery concept through energy digitization, traditional battery systems can be transformed into reconfigurable battery networks (RBNs). This RBN paradigm improves the inherent defects of the fixed battery system, which means the performance of the RBN will no longer be limited by the consistency of the cells. In this article, an adaptive control framework with the asynchronous advantage actor-critic (A3C) paradigm on performing online optimization for the dynamical RBN system is proposed. By utilizing its policy and asynchronous learning property, the proposed paradigm can improve the learning performance and the battery pack capacity through an improvement of cell consistency. It is noted that the proposed method is verified by both the simulation and experiment with data from an RBN.

关键词： Batteries State of charge Transportation Optimization Topology Network systems linear programming Asynchronous advantage actor-critic (A3C) large-scale model-free reconfigurable battery network (RBN)

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A Full-Waveform Inversion Method Based on Structural Tensor Constraints

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Liu, Fengliang Zhou, Hui Chen, Hanming Wang, Lingqian Fu, Yuxin China Univ Petr Natl Key Lab Petr Resources & Engn Beijing 102249 Peoples R China China Univ Petr CNPC Key Lab Geophys Explorat Beijing 102249 Peoples R China China Univ Petr Natl Key Lab Petr Resources & Engn CNPC Key Lab Geophys Explorat Beijing 102249 Peoples R China China Univ Petr Coll Sci Beijing 102249 Peoples R China

Full-waveform inversion (FWI) is a method for obtaining velocity models. Because of its theoretical completeness, the final modeling results are often superior to those of velocity modeling methods such as stacking velocity analysis, migration velocity analysis, and velocity tomography. FWI usually takes the L2 norm of the residuals between observed and simulated data as the objective function. The current velocity model that best fits the observed data is obtained by solving the objective function. However, during FWI, there is a strong ill-posedness due to its limited conditions. To solve this problem, regularized constraints or gradient preconditioning methods are commonly introduced. In this article, we propose a regularization method that incorporates prior information, aiming to alleviate the aforementioned issues to some extent. This method adds a regularization term based on structural tensors to the original L2 norm objective function, introducing subsurface structural information in conventional regularized constraints of FWI. It smooths the velocity model along layer interfaces to ensure that the obtained inversion results are more consistent with the true velocity model. The inversion results from synthetic data of the Marmousi model and BP 2004 benchmark velocity model demonstrate the feasibility and effectiveness of this method. The noisy data FWI further demonstrates the robustness of this method. Moreover, the proposed method can better recover the small-scale structures in the velocity model and significantly improve the resolution of the velocity model.

关键词： linear programming Tensors Computational modeling Data models Stratigraphy Optimization Geoscience and remote sensing Eigenvalues and eigenfunctions Accuracy Computational efficiency Full-waveform inversion (FWI) objective function regularization structure tensor velocity modeling

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