Search Results (434)

Generalized Sinusoidal Frequency Modulation (GSFM) signals can enhance Continuous Active Sonar (CAS) performance by providing high sub-signal processing gain while achieving high target update rates. However, conventional processing methods for GSFM often exhibit high sidelobe levels arising from the waveform’s autocorrelation which degrade detection performance, especially in severe multipath environments. To address this issue, a Mismatched-Channel Filtering (MMCF) method for GSFM in CAS is proposed to focus multipath energy while suppressing sidelobe levels. Adopting the sub-pulse processing scheme, we incorporate the orthogonality of GSFM sub-signals (optimized via a genetic algorithm) and sparse channel estimates into the MMCF design for each sub-signal. The design is formulated as a Quadratically Constrained Quadratic Program (QCQP) and solved iteratively using the Alternating Direction Method of Multipliers (ADMM) for long-duration signal processing in CAS. Numerical simulations demonstrate that, compared with the matched filtering and matched channel filtering methods, the proposed MMCF method effectively suppresses sidelobe levels by approximately 20 dB and produces a Dirack-like main-lobe peak, while efficiently focusing multipath energy. The method’s effectiveness is further validated using experimental data from a lake trial. Therefore, this algorithm has distinct advantages for signal processing in multipath environments. Full article

Low-rank recovery has emerged as a powerful methodology for the restoration of degraded images. Conventional low-rank recovery techniques, however, predominantly rely on nuclear norm or weighted nuclear norm minimization to separate sparse noise. A significant limitation of this approach is its dependence on full singular value decomposition, which imposes a substantial computational burden, thereby hindering its practical applicability. This paper presents a novel image denoising model integrating the weighted nuclear norm and deep image prior. The weighted nuclear norm is introduced to accurately characterize the global structural properties of images, ensuring the consistency of the overall image structure after denoising. Meanwhile, the deep image prior is employed to effectively capture local details, which helps avoid the blurring of textures and edges often caused by excessive noise removal. The complementary advantages of the two components enable the proposed model to achieve superior performance compared with existing denoising methods. To efficiently compute the proposed model, we design the bilinear factorization method and the alternating direction method of multipliers. Experiments show that the proposed method outperforms mainstream approaches in both restoration accuracy and computational efficiency, exhibiting rapid convergence and robust algorithm stability, thereby demonstrating excellent comprehensive performance. Full article

Rapid increases in distributed photovoltaic (PV) penetration have brought additional challenges to distribution network planning and operation. Meanwhile, flexible interconnection devices such as soft open point integrated with battery energy storage system (E-SOP) can significantly enhance the regulatory capability and operational adaptability of the distribution system and have been widely applied in recent years. First, to improve both economic performance and voltage quality, a coordinated planning method for the multi-region flexible interconnected distribution system based on E-SOP is proposed. Second, with the ongoing growth of interconnected distribution networks, centralized optimization methods exhibit limitations in computational efficiency and privacy protection. To address this, the planning model is decomposed into several subproblems by applying the Alternating Direction Method of Multipliers (ADMM), allowing each region to optimize its local subproblem in a fully distributed manner. Additionally, a Shapley value-based cost allocation mechanism is applied to ensure fair and rational cost distribution among different distribution networks. Finally, case studies are conducted to validate the effectiveness of the proposed method. Case studies show that the proposed method reduces the system’s total annual cost by 14.90% and the electricity purchase cost by 28.61% compared with the pre-planning case. Meanwhile, the maximum voltage imbalance is reduced to within the standard range. These results validate the effectiveness of the proposed method in enhancing both economic efficiency and power quality for flexible interconnected distribution systems. Full article

The large-scale integration of renewable energy necessitates enhanced flexibility in power grids. As aggregators, electric vehicle charging stations (EVCSs) can provide potential grid services via vehicle-to-grid (V2G) technology. Against the challenge from the intertwined uncertainties of transmission system operation and renewable energy output limit, the private ownership of EVCSs limit their practical implementation. To exploit the flexibility of EVCSs to cope with the system operational uncertainties, this paper proposes a novel multi-stage coordination framework for EVCS flexibility utilization, based on a demand response incentive mechanism. The framework explicitly incorporates the operational constraints and charging/discharging strategies of EVCSs into the demand response clearing and dispatch mechanism. Specifically, adaptive robust optimization (ARO) and distributionally robust optimization (DRO) are employed to model the heterogeneous uncertainties of transmission operational requirements and renewable energy output, respectively. The model is solved in a distributed manner using the alternating direction method of multipliers (ADMM), with a tailored column-and-constraint generation (C&CG) algorithm developed to solve the regional problems. Simulation results confirm that the proposed method improves both economic efficiency and renewable energy accommodation. Full article

To address the issue of missing position in flight trajectory data collected by Automatic Dependent Surveillance-Broadcast (ADS-B) systems, a flight trajectory tensor completion model based on truncated Schatten p-norm minimization is proposed. First, the low-rank characteristics of the trajectory set are validated using Singular Value Decomposition (SVD); based on this, the data is transformed into a three-dimensional tensor structure. Next, a regularization strategy combining the Schatten p-norm with a singular value truncation mechanism is introduced to construct the trajectory tensor completion model, which suppresses noise and interference from minor components while preserving the main variation patterns of the trajectories. Finally, the model is optimized and solved using the Alternating Direction Method of Multipliers (ADMM) to obtain the completed trajectories. Taking historical ADS-B trajectory data from Orly Airport to Toulouse Airport as an example, the completion results of the proposed model under different missing patterns, missing rates, and flight phases are analyzed from both qualitative and quantitative perspectives. Experimental results show that compared with other representative models, the proposed model achieves the best completion performance under different missing patterns and missing rates; the completion performance during the cruise phase is better than during the ascent and descent phases. The proposed model can serve as a preprocessing technique for flight trajectory data in air traffic, providing more complete and reliable data support for various downstream applications. Full article

Total variation (TV)-based image denoising effectively suppresses noise while preserving edges, but it often introduces staircase artifacts in flat regions. To address this limitation, we propose a novel denoising model that combines adaptive fractional-order total variation with a minimax-concave (MC) penalty in the regularization term. The adaptive fractional-order TV alleviates staircase effects in homogeneous areas while preserving fine details in textured regions. The MC penalty provides a more accurate estimation of image sparsity, improving restoration fidelity compared to traditional L1-based regularization. The resulting model, termed AFTVMC, is efficiently solved using an alternating direction method of multipliers (ADMM). Extensive numerical experiments on synthetic and natural images demonstrate that AFTVMC outperforms classical TV, higher-order LLT, adaptive ATV, and state-of-the-art MCFOTV models in both objective metrics—peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM)—and subjective visual quality, particularly in suppressing staircase artifacts and preserving complex texture details. Full article

With the widespread integration of smart buildings equipped with distributed photovoltaics (PV) and electric vehicles (EVs), distribution networks face significant challenges arising from source-load fluctuations. Conventional centralized dispatch approaches are constrained by communication bottlenecks and data privacy requirements. These limitations make it difficult to achieve global coordination while preserving the autonomy of individual entities. This paper proposes a hierarchical coordination framework for the coordinated operation of distribution networks and smart buildings. The distribution management system (DMS) and building energy management systems (BEMSs) perform independent optimization within their respective domains. Only aggregated boundary power information is exchanged to protect data privacy, enabling cross-entity coordination under information boundary constraints. Building-side models incorporating thermal dynamics, EV charging and discharging, and PV generation are developed, along with a distribution network power flow model. To solve the coordinated optimization problem, an Anderson-accelerated alternating direction method of multipliers (AA-ADMM) is introduced. A safeguarding mechanism based on combined residuals is incorporated to enhance convergence efficiency and stability. Case studies on the IEEE 33-bus test system demonstrate that compared with the uncoordinated baseline, the proposed method reduces network loss by 12.1% and lowers PV curtailment from 9.20% to 0.52%, while improving voltage profiles without significantly compromising occupant comfort or EV travel requirements. In addition, AA-ADMM achieves convergence with up to 66% fewer iterations than standard ADMM. Full article

Multi-regional electricity markets increasingly struggle to balance data privacy requirements with the computational burden of centralized clearing. To address this issue, this study proposes a distributed joint-clearing framework based on the Alternating Direction Method of Multipliers (ADMM) to co-optimize pumped storage hydropower (PSH) and battery energy storage systems (BESS) across energy, frequency regulation, and reserve markets. A mixed-integer programming model is formulated to maximize social welfare, explicitly capturing the time-coupled, energy-oriented characteristics of PSH and the fast-response, power-oriented capabilities of BESS. The global problem is decomposed into regional subproblems that can be solved in parallel. An adaptive penalty parameter strategy is further introduced to dynamically balance primal and dual residuals, thereby improving convergence and robustness in the mixed-integer setting. To address the limited economic interpretability of dual variables in mixed-integer programming (MIP) models, an approximate marginal pricing mechanism based on subproblem sensitivity analysis is proposed. A two-region, 24 h case study shows that the proposed method converges in around 65 iterations and achieves a social welfare outcome within 0.61% of the centralized optimum. By minimizing information exchange, the framework offers a scalable and privacy-aware solution for future multi-regional market operations involving heterogeneous energy storage resources. Full article

Operations scheduling of mass customized products is vital in the modern make-to-order (MTO) supply chains. In these systems, order acceptance decisions should be coordinated with available capacity in different sections of the supply chain while considering their potential correlations and interactions. One of the fundamental challenges in optimization of these systems is the computation time of solving models with multiple coupling constraints between supply chain units. This paper addresses this issue by proposing a game-based framework that decomposes the related mixed integer programming mathematical model and it is coordinated and solved using integrated game-based Alternating Direction Method of Multipliers (ADMM). The proposed Stackelberg Leader-Follower game optimizes order acceptance decisions while considering the requirements in supply, production planning, maintenance, inventory, and distribution units. To validate the efficiency of the proposed framework, the model is tested with a simulated four-layer supply chain. The results of experiments proved that decompositions of the model to smaller subsections and solving it in a distributed manner not only optimizes supply chain participating units but also coordinate their movements to achieve the global optimal solution. The proposed framework offers managers a practical decision layer that preserve local autonomy of the supply chain units and reduce their data sharing and computation burdens and concerns. Full article

To enhance the performance of deep image prior, we propose a novel image denoising model that embeds an anisotropic diffusion tensor into the total generalized variation model and combines it with the deep image prior. The proposed tensor weights deep gradients and guides gradient orientation, which effectively preserves sharp edges. We solve the corresponding minimization problem using the augmented Lagrangian method and the alternating direction method of multipliers. Experimental results show that the proposed method can remove noise while suppressing staircase artifacts and enhancing edge structures, yielding restored images with clearer edge details. Both quantitative metrics and visual comparisons show consistent improvements over competing methods across multiple noise levels, with more pronounced advantages in edge preservation. Full article

For interconnected multi-microgrids, it is crucial to improve operational economy and renewable energy utilization while ensuring system security. However, existing studies still face limitations in handling multi-time-scale uncertainties and enhancing the incentive for energy trading. Therefore, this paper proposes a cooperative distributed energy management strategy for interconnected microgrids based on model predictive control. First, a multi-time-scale framework is introduced into the multi-microgrid model, where rolling optimization and adaptive prediction/control horizons are used to cope with stochastic fluctuations of sources and loads. Then, a cooperative game model for the multi-microgrid coalition is formulated, and the asymmetric Nash bargaining problem is equivalently decomposed into a two-stage procedure of “coalition operation cost minimization–transaction bargaining”. Next, an algorithm for a distributed alternating-direction method of multipliers is employed for solution. Finally, multi-scenario simulations are carried out to compare three operation modes: independent operation, cooperation only, and model predictive control-based cooperation. The results show that compared with the independent operation mode, the total operation cost of the system is reduced by 22.8% using the proposed method and by 6.3% compared with the mode only adopting the cooperation mechanism, which demonstrates the effectiveness of the proposed strategy. The proposed strategy also enhances sustainability by improving local renewable energy accommodation, reducing reliance on upstream grid electricity, and supporting more resilient operation of interconnected microgrids under uncertainty. Full article

To address the challenges of insufficient frequency regulation capability of individual stations, poor collaborative economic performance, and unfair benefit allocation caused by fluctuations in photovoltaic (PV) output and variations in electric vehicle (EV) connectivity during vehicle-to-grid (V2G) interactions under high-penetration PV integration, this paper proposes a coordinated optimal operation strategy for peer-to-peer (P2P) energy sharing and joint frequency regulation among multiple electric vehicle charging stations (EVCSs). First, a collaborative framework for P2P energy sharing and joint frequency regulation among EVCSs is constructed to describe the operational mechanism of inter-station energy mutual support and coordinated response to frequency regulation signals. Subsequently, an aggregate model of the dispatchable potential for EV clusters within each station is established based on Minkowski Summation (M-sum), characterizing the charging and discharging power boundaries and frequency regulation potential of the EV clusters. Meanwhile, distributionally robust chance constraints (DRCC) based on the Kullback–Leibler (KL) divergence are introduced to handle the uncertainty of PV power generation within the EVCS. On this basis, a dynamic frequency regulation output model for EV clusters and a multi-station P2P energy sharing model are designed, with the optimization objective of minimizing the total operating cost. Finally, to quantify the differential contributions of each EVCS in the collaborative operation, an asymmetric Nash bargaining benefit allocation mechanism is proposed, which incorporates a comprehensive contribution index considering both energy sharing and joint frequency regulation, The model is solved in a distributed manner using the alternating direction method of multipliers (ADMM). Simulation results demonstrate that, compared to non-cooperative operation, the frequency regulation completeness rates of the EVCSs after cooperation increase by 5.7%, 5.2%, and 4.4%, respectively; meanwhile, the total operating cost drops from CNY 16,187.61 under non-cooperative operation to CNY 15,997.47, achieving a reduction of 1.18%. The proposed strategy not only meets grid frequency regulation demands but also enhances the economic efficiency of multi-station collaborative operation and the fairness of benefit distribution. Full article

Traditional mean–variance portfolio optimization proves inadequate for cryptocurrency markets, where extreme volatility, fat-tailed return distributions, and unstable correlation structures undermine the validity of variance as a comprehensive risk measure. To address these limitations, this paper proposes a unified entropy-based portfolio optimization framework grounded in the Maximum Entropy Principle (MaxEnt). Within this setting, Shannon entropy, Tsallis entropy, and Weighted Shannon Entropy (WSE) are formally derived as particular specifications of a common constrained optimization problem solved via the method of Lagrange multipliers, ensuring analytical coherence and mathematical transparency. Moreover, the proposed MaxEnt formulation provides an information-theoretic interpretation of portfolio diversification as an inference problem under uncertainty, where optimal allocations correspond to the least informative distributions consistent with prescribed moment constraints. In this perspective, entropy acts as a structural regularizer that governs the geometry of diversification rather than as a direct proxy for risk. This interpretation strengthens the conceptual link between entropy, uncertainty quantification, and decision-making in complex financial systems, offering a robust and distribution-free alternative to classical variance-based portfolio optimization. The proposed framework is empirically illustrated using a portfolio composed of major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB)—based on weekly return data. The results reveal systematic differences in the diversification behavior induced by each entropy measure: Shannon entropy favors near-uniform allocations, Tsallis entropy imposes stronger penalties on concentration and enhances robustness to tail risk, while WSE enables the incorporation of asset-specific informational weights reflecting heterogeneous market characteristics. From a theoretical perspective, the paper contributes a coherent MaxEnt formulation that unifies several entropy measures within a single information-theoretic optimization framework, clarifying the role of entropy as a structural regularizer of diversification. From an applied standpoint, the results indicate that entropy-based criteria yield stable and interpretable allocations across turbulent market regimes, offering a flexible alternative to classical risk-based portfolio construction. The framework naturally extends to dynamic multi-period settings and alternative entropy formulations, providing a foundation for future research on robust portfolio optimization under uncertainty. Full article

Spacecraft pursuit–evasion in contested environments is complicated by strategic incompleteness: the evader can switch maneuvering modes and deploy multi-domain countermeasures that degrade the pursuer’s perception, leading to non-stationary information and distributionally ambiguous interference statistics. A dynamic time-window Nash equilibrium framework is developed for linearized Local Vertical Local Horizontal (LVLH) relative motion under interference-induced uncertainty. Perceptual degradation is modeled via an evidence–theoretic belief representation, and the Jensen–Shannon (JS) divergence is introduced to quantify discrepancies between nominal and interference-corrupted beliefs. The divergence metric drives an adaptive time-window partitioning policy and an uncertainty-aware running cost that balances nominal performance objectives with robustness regularization during high-degradation intervals. In each time window, sufficient conditions are provided for the existence of a local Nash equilibrium, and equilibrium strategies are characterized by the Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation. A global consistency result is established: assuming state continuity, additive cost decomposition, and dynamic-programming compatibility at window boundaries, concatenating the window-wise equilibria yields a Nash equilibrium over the entire horizon. Unlike conventional receding-horizon differential games with a fixed replanning grid, the proposed policy partitions the horizon online in response to perceptual-degradation events and stitches adjacent windows through a continuation value. This boundary stitching enables the global consistency guarantee under additive costs and state continuity. To hedge against ambiguity in interference intensity, a variational distributionally robust optimization (DRO) problem with moment-constrained ambiguity sets is formulated, and the dual worst-case distribution is derived. The resulting Karush–Kuhn–Tucker (KKT) system is reformulated as a finite-dimensional variational inequality, for which an accelerated Alternating Direction Method of Multipliers (ADMM) operator-splitting solver is proposed for efficient real-time computation. Numerical simulations validate the framework and demonstrate improved robustness and computational scalability under time-varying interference compared with fixed-window baselines. Full article

Topology identification and signal inference are cornerstone tasks in graph signal processing (GSP). Structural Equation Modeling (SEM) is particularly effective for network inference as it explicitly captures causal dependencies. However, a major bottleneck in existing SEM-based approaches is the reliance on fully observable exogenous inputs. In many practical applications, systems are driven by latent stimuli, rendering traditional estimation methods ineffective. To overcome this, we propose a novel SEM framework for the joint inference of graph topology and unknown exogenous inputs. The core innovation lies in the spatio-temporal modeling of these latent inputs: each stimulus is decomposed into a rank-one component characterized by nodal sparsity (spatial localization) and temporal piecewise smoothness (temporal persistence). This structured formulation transforms an otherwise ill-posed blind identification problem into a tractable regularized optimization task. We develop an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve the resulting convex problem. Numerical experiments on synthetic and real-world datasets demonstrate that the proposed method effectively disentangles endogenous network interactions from latent exogenous influences, outperforming baseline approaches in both topology and signal recovery. Full article