Restoration Problem Research Articles

Closing the gap on wicked urban stream restoration problems: A framework to integrate science and community values

Restoring the health of urban streams has many of the characteristics of a wicked problem. Addressing a wicked problem requires managers, academics, practitioners, and community members to make negotiated tradeoffs and compromises to satisfy the values and perspectives of diverse stakeholders involved in setting restoration project goals and objectives. We conducted a gap analysis on 11 urban stream restoration projects to identify disconnections, underperformance issues, and missing processes in the project structures used to develop restoration project goals and objectives. We examined the gap analysis results to investigate whether managers appropriately identified problem statements and met stated objectives. Projects that aimed to restore overall stream health commonly fell short for various reasons, including limited stakeholder and community input and buy-in, revealing potential limitations in the breadth of objectives, values, and stakeholder perspectives and knowledge types. Projects that emphasized integrating community values and diverse knowledge types tended to meet the expected outcomes of restoring stream processes through incremental solutions. Managers implementing more holistic solutions and values-driven approaches are more likely to consider diverse viewpoints from a variety of community local institutions. Based on these and other results, we propose a conceptual framework that integrates diverse perspectives and knowledge to enhance social and ecological outcomes of urban stream restoration. The framework also emphasizes the importance of setting objectives that support incremental solutions to foster more realistic expectations amongst stakeholders.

Fault Detecting and Isolating Schemes in a Low-Voltage DC Microgrid Network from a Remote Village

Fault detection and isolation are important tasks to improve the protection system of low voltage direct current (LVDC) networks. Nowadays, there are challenges related to the protection strategies in the LVDC systems. In this paper, two proposed methods for fault detection and isolation of the faulty segment through the line and bus voltage measurement were discussed. The impacts of grid fault current and the characteristics of protective devices under pre-fault normal, under-fault, and post-fault conditions were also discussed. It was found that within a short time after fault occurrence in the network, this fault was quickly detected and the faulty line segment was efficiently isolated from the grid, where this grid was restored to its normal operating conditions. For analysing the fault occurrence and its isolation, two algorithms with their corresponding MATLAB/SIMULINK platforms were developed. The findings of this paper showed that the proposed methods would be used for microgrid protection by successfully resolving the fault detection and grid restoration problems in the LVDC microgrids, especially in rural villages.

Image Restoration using Nonlocal Regularized Variational Model with Spatially Adapted Regularization Parameter

Restoration of blurred images corrupted with impulse noise has attracted increasing attention due to its great potential toward practical applications. To guarantee the noisy image deblurring performance, total variation (TV), one of the most popular regularizers, has been extensively exploited in the literature. This local regularizer is known for its capability of preserving edges and penalizing oscillations but not sharp discontinuities. However, TV regularized variational models commonly suffer from undesirable staircase-like artifacts in homogeneous regions. The reason behind this phenomenon is that TV regularizer favors solutions that are piecewise constant in numerical experiments. To overcome the model-dependent deficiency, we proposed to replace the commonly used local regularizer by its nonlocal extension, i.e., nonlocal total variation (NLTV) regularizer. By taking advantage of the high degree of self-similarity within images, the proposed nonlocal variational model is able to remove both blurring and impulse noise while preserving meaningful image details. To guarantee solution stability and efficiency, the resulting nonsmooth image restoration problem was effectively dealt with using an alternating optimization algorithm. In addition, to further enhance the image quality, a local variance estimator-based automatic method was introduced to calculate the spatially adapted regularization parameters during image restoration. Extensive experiments have demonstrated that the proposed method could achieve superior imaging performance compared with several state-of-the-art image restoration methods.

Distribution network supply restoration using measurement-based robust optimization approach

Most of the approaches developed to solve the supply restoration problem are based on the power flow models. Therefore, the practical implementation of these approaches requires a complete and accurate set of distribution network models, which are not available in many distribution networks. This paper proposes a new measurement-based approach for supply restoration which uses: (1) real-time current measurements gathered by the distribution system control center, and (2) topology of the distribution network, without requiring any other model information. The proposed approach formulates the supply restoration as the robust optimization problem in order to consider uncertainty involved with the used current measurements. The approach is applicable in distribution networks with distributed generators. The robustness and efficiency of the proposed methodology is demonstrated by calculating both a small test network and a 123-node network.

An accelerated common fixed point algorithm for a countable family of G-nonexpansive mappings with applications to image recovery

In this paper, we define a new concept of left and right coordinate affine of a directed graph and then employ it to introduce a new accelerated common fixed point algorithm for a countable family of G-nonexpansive mappings in a real Hilbert space with a graph. We prove, under certain conditions, weak convergence theorems for the proposed algorithm. As applications, we also apply our results to solve convex minimization and image restoration problems. Moreover, we show that our algorithm provides better convergence behavior than other methods in the literature.

Обґрунтування підходів і стратегічних напрямів щодо секвестрації й збільшення органічного вуглецю в ґрунтах зони Полісся

Goal. To scientifically substantiate zonal approaches and directions of organic carbon management in the soils of the Polissia zone and search for ways of its sequestration, restoration of soil fertility, and reduction of carbonic acid production in the atmosphere, which will be taken as a basis for the formation of key carbon farming systems in this area. Methods. System approach, scientific analysis of information, comparison, theoretical and practical generalization, abstract-logical. Results. Problems of restoration of humus reserves in low-yielding sod-podzolic soils of the Polissia zone, and preservation of their ecological and energy potential are analyzed. The possibilities and expediency of organic carbon management at the regional level and its sequestration in the soils of the Polissia zone are highlighted. Scientific approaches and strategic directions for the reproduction, absorption, and retention of organic carbon in sod-podzolic soils to ensure neutral land degradation are considered. The main ways of development of carbon agriculture at the regional level to achieve the stabilization of organic carbon in the soils of the Polissia zone and its further increase are proposed. Conclusions. Carbon uptake and retention in the soil reservoir are ensured through appropriate zonal farming systems that regulate soil fertility. This problem is solved by arranging arable land, introducing low-carbon use with reforestation, stimulating livestock development, including the creation of hayfields and pastures, increasing production, and applying optimal doses of organic and mineral fertilizers, including secondary organic raw materials and local natural resources. As well as changing the structure of sown areas with a reduction of row crops, increasing the share of perennial grasses, the inclusion of legumes and cover and green crops in crop rotations, increasing crop yields, and introducing technologies of minimal tillage. With the application of anti-degradation measures, agro-technologies of soil cultivation, and involvement of all renewable organic resources, a positive balance of humus is achieved, which is a prerequisite for reducing carbon emissions, restoring and increasing soil fertility and productivity of agrocenoses.

A Hybrid Conjugate Gradient Algorithm for Nonconvex Functions and Its Applications in Image Restoration Problems

A Hybrid Conjugate Gradient Algorithm for Nonconvex Functions and Its Applications in Image Restoration Problems

Scaled Three-Term Conjugate Gradient Methods for Solving Monotone Equations with Application

In this paper, we derived a modified conjugate gradient (CG) parameter by adopting the Birgin and Marti´nez strategy using the descent three-term CG direction and the Newton direction. The proposed CG parameter is applied and suggests a robust algorithm for solving constrained monotone equations with an application to image restoration problems. The global convergence of this algorithm is established using some proper assumptions. Lastly, the numerical comparison with some existing algorithms shows that the proposed algorithm is a robust approach for solving large-scale systems of monotone equations. Additionally, the proposed CG parameter can be used to solve the symmetric system of nonlinear equations as well as other relevant classes of nonlinear equations.

A new preconditioning algorithm for finding a zero of the sum of two monotone operators and its application to image restoration problems

ABSTRACT Finding a zero of the sum of two monotone operators is one of the most important problems in monotone operator theory, and the forward-backward algorithm is the most prominent approach for solving this type of problem. The aim of this paper is to present a new preconditioning forward-backward algorithm to obtain the zero of the sum of two operators in which one is maximal monoton and the other one is M-cocoercive, where M is a linear bounded operator. Furthermore, the strong convergence of the proposed algorithm, which is a broader variant of previously known algorithms, has been proven in Hilbert spaces. We also use our algorithm to tackle the convex minimization problem and show that it outperforms existing algorithms. Finally, we discuss several image restoration applications.

Resilience assessment of railway networks: Combining infrastructure restoration and transport management

During railways operations, unplanned events might occur which can result in rail traffic being heavily impacted. The paper proposes a passenger-centred resilience assessment for disruption scenarios which consist of multiple simultaneous disruptions. It combines train traffic operations, passenger flows and network restoration. To evaluate resilience, an optimization-based approach has been developed for solving the new infrastructure restoration and transport management (IRTM) problem. Additionally, this approach develops mitigation plans for the best infrastructure restoration and traffic recovery and it captures the time-dependent transport network performance during disruptions. The approach is general with respect to types of disruptions, and can be applied for evaluation against short disruptions (1–2 h) as well as more substantial ones (multiple days or weeks). The performance of the proposed approach has been demonstrated on a Dutch railway network. Furthermore, the resilience of the system is assessed against the critical infrastructure disruption scenarios in the network. This optimization-based approach shall enable decision makers to quantify accurately impacts of multiple disruptions by considering the created inconveniences to passengers in the railway operation due to these disruptions.

The Magnetic Urtext: Restoration as Music Interpretation.

This paper discusses historical-critical thought to address the problems of restoration and preservation of tape music, proposing viable solutions to the matter of digitizing the historically valuable data that exists on and is represented by magnetic tapes. A detailed program of research and restoration and some software for helping in creation of critical editions of the musical works are proposed. We also present some of the issues and controversies that must be considered and approaches we have applied in the preservation of tape music, highlighting how these interpretations can impact later performances (playback) of these tape documents. Fundamentally, we argue that the act of tape music restoration has a parallel with the interpretation of the “Urtext score” in the performance of music from the Common Practice Era.

(Retracted) Method of generating face image based on text description of generating adversarial network

Generative adversarial networks (GANs) can generate sentences that imitate real language features from discrete spaces, and the implicit expression space learned by training can continuously generate credible sentences. We solve the problem of face image restoration of different genders, different skin colors, and different hair colors through a method of generating face images, in which the face image generated by text description has a better generation effect. We propose a face image generation method (T-GAN) based on generating confrontational text. Due to the particulars of a face, a Word-long short-term memory model is used to extract the text features corresponding to the face. Then combined with the idea of generating a game against “game,” a counter-text network is created, next a third type of input is added to the discriminator, and the real image with unmatched text is composed to strengthen the discriminator’s training effect and force discrimination. The device determines whether the generated image conforms to the text description, so the discriminator can better learn the relationship between the text description and the image content. The experimental results show that, compared with other image generation methods, the face image generated by the proposed method can give high quality generated images with a better effect and less distortion, which can better maintain the original attributes of the image, has the use value, and increases the reduction degree by 3.84%.

A descent extension of a modified Polak–Ribière–Polyak method with application in image restoration problem

A descent extension of a modified Polak–Ribière–Polyak method with application in image restoration problem

General nonconvex total variation and low-rank regularizations: Model, algorithm and applications

Total Variation and Low-Rank regularizations have shown significant successes in machine learning, data mining, and image processing in past decades. This paper develops the general nonconvex composite regularized model, which contains previous regularizers and motivates novel ones. Although the classical Alternating Direction Methods of Multiplier (ADMM) algorithm is applicable for this model, the nonconvexity of the problem and the complicacy of choosing the parameters increase the difficulty in the use of ADMM. Thus, by the penalty method, we propose the Alternating Minimization (AM) algorithm, whose convergence results are proved under mild assumptions. The proposed model and algorithm are applied to the image restoration problem. Numerical results demonstrate the efficiency of our model and algorithm.

Model and solution method for mean-risk cost-based post-disruption restoration of interdependent critical infrastructure networks

Critical infrastructure networks (CINs), such as power grids, water distribution systems, and telecommunication networks, are essential for the functioning of society and the economy. As these infrastructure networks are not isolated from each other, their functions are not independent and may be vulnerable to disruptive events (e.g., component failures, terrorist attacks, natural disasters). For decision makers, how to restore the functions of CINs while accounting for interdependencies and various uncertainties becomes a challenging task. In this work, we study the post-disruption restoration problem for a system of interdependent CINs under uncertainty. We propose a two-stage mean-risk stochastic restoration model using mixed-integer linear programming (MILP) with the goal of minimizing the total cost associated with unsatisfied demands, repair tasks, and flow of interdependent infrastructure networks. The restoration model considers the availability of limited time and resources and provides a prioritized list of components to be restored along with assigning and scheduling them to the available network-specific work crews. Additionally, the model features flexible restoration strategies including multicrew assignment for a single component and a multimodal repair setting along with the consideration of full and partial functioning and dependencies between the multi-network components. The proposed model is illustrated using the power and water networks in Shelby County, Tennessee, United States, under two hypothetical earthquake scenarios.

DICDNet: Deep Interpretable Convolutional Dictionary Network for Metal Artifact Reduction in CT Images.

Computed tomography (CT) images are often impaired by unfavorable artifacts caused by metallic implants within patients, which would adversely affect the subsequent clinical diagnosis and treatment. Although the existing deep-learning-based approaches have achieved promising success on metal artifact reduction (MAR) for CT images, most of them treated the task as a general image restoration problem and utilized off-the-shelf network modules for image quality enhancement. Hence, such frameworks always suffer from lack of sufficient model interpretability for the specific task. Besides, the existing MAR techniques largely neglect the intrinsic prior knowledge underlying metal-corrupted CT images which is beneficial for the MAR performance improvement. In this paper, we specifically propose a deep interpretable convolutional dictionary network (DICDNet) for the MAR task. Particularly, we first explore that the metal artifacts always present non-local streaking and star-shape patterns in CT images. Based on such observations, a convolutional dictionary model is deployed to encode the metal artifacts. To solve the model, we propose a novel optimization algorithm based on the proximal gradient technique. With only simple operators, the iterative steps of the proposed algorithm can be easily unfolded into corresponding network modules with specific physical meanings. Comprehensive experiments on synthesized and clinical datasets substantiate the effectiveness of the proposed DICDNet as well as its superior interpretability, compared to current state-of-the-art MAR methods. Code is available at https://github.com/hongwang01/DICDNet.

Two modified conjugate gradient methods for unconstrained optimization with applications in image restoration problems

Two modified conjugate gradient methods for unconstrained optimization with applications in image restoration problems

The Modified Viscosity Approximation Method with Inertial Technique and Forward–Backward Algorithm for Convex Optimization Model

In this paper, we propose a new accelerated algorithm for finding a common fixed point of nonexpansive operators, and then, a strong convergence result of the proposed method is discussed and analyzed in real Hilbert spaces. As an application, we create a new accelerated viscosity forward–backward method (AVFBM) for solving nonsmooth optimization problems of the sum of two objective functions in real Hilbert spaces, and the strong convergence of AVFBM to a minimizer of the sum of two convex functions is established. We also present the application and simulated results of AVFBM for image restoration and data classification problems.

An Accelerated Fixed-Point Algorithm with an Inertial Technique for a Countable Family of G-Nonexpansive Mappings Applied to Image Recovery

Many authors have proposed fixed-point algorithms for obtaining a fixed point of G-nonexpansive mappings without using inertial techniques. To improve convergence behavior, some accelerated fixed-point methods have been introduced. The main aim of this paper is to use a coordinate affine structure to create an accelerated fixed-point algorithm with an inertial technique for a countable family of G-nonexpansive mappings in a Hilbert space with a symmetric directed graph G and prove the weak convergence theorem of the proposed algorithm. As an application, we apply our proposed algorithm to solve image restoration and convex minimization problems. The numerical experiments show that our algorithm is more efficient than FBA, FISTA, Ishikawa iteration, S-iteration, Noor iteration and SP-iteration.

On some accelerated optimization algorithms based on fixed point and linesearch techniques for convex minimization problems with applications

In this paper, we introduce and study a new accelerated algorithm based on forward–backward and SP-algorithm for solving a convex minimization problem of the sum of two convex and lower semicontinuous functions in a Hilbert space. Under some suitable control conditions, a weak convergence theorem of the proposed algorithm based on a fixed point is established. Moreover, we choose the stepsize of our algorithm which is independent on the Lipschitz constant of the gradient of the objective function by using a linesearch technique, and then a weak convergence result of the proposed algorithm is analyzed. As applications, we apply the proposed algorithm for solving the image restoration problems and compare its convergence behavior with other well-known algorithms in the literature. By our experiment, the algorithms have a higher efficiency than the others.