Equalization Method Research Articles

Risk assessment for hurricane-induced pluvial flooding in urban areas using a GIS-based multi-criteria approach: A case study of Hurricane Harvey in Houston, USA

As one of the most destructive nature hazards, hurricane-induced flooding generates serious adverse impacts on populations, infrastructure, and the environment globally. In urban areas, complex characteristics such as high population and infrastructure densities increase flood disaster risks. Consequently, the assessment of flood risks is becoming increasingly important for understanding potential impacts on an urban area and proposing disaster risk mitigation strategies. After conducting a comprehensive literature review, this study finds that most urban flood risk assessments often overlook urban ecosystem elements, focusing more on social and economic aspects. Hence, the role of urban ecosystems cannot be fully understood. To address this gap, this study proposes a social-ecological systems (SES) flood risk assessment framework for urban areas. Based on this framework, a comprehensive list of indicators collected through a literature review is provided for urban flood risk assessments. A comparative study of flood risk during Hurricane Harvey (2017) in Houston, Texas, USA, is carried out using the improved analytic hierarchy process (IAHP) weighting method and the equal weighting method for indicator weighting. Results are then compared with the damage data of Hurricane Harvey published by the U.S. Federal Emergency Management Agency (FEMA). The analysis identifies that the western part of Houston had the highest flood risks, while the center of Houston was at lower flood risk. Comparisons between the results from the IAHP and equal weighting methods show that the latter produces a broader range of high flood risk areas than the former. This study also highlights the role of urban ecosystems in mitigating flood risks and advocates for more holistic, social-ecological assessments of flood risk. Such assessments could utilize the proposed framework and the indicator list but contextualize these to the specific urban area's contexts being investigated.

The splitting-based semi-implicit finite-difference schemes for simulation of blood flow in arteries

This paper is devoted to construction and analysis of splitting-based finite-difference schemes for simulation of blood flow in applications of one-dimensional models. Proposed schemes are constructed for inviscid and viscid models of blood. The proposed approach is based on the modification of averaged incompressibility condition, rewritten for the square root of a cross-sectional area. It is approximated by an absolutely stable scheme with a skew-symmetric operator. The constructed schemes can be classified as semi-implicit. The stability of proposed schemes is analyzed by the method of energy inequalities. Sufficient stability conditions are obtained. For the problems with analytical solutions, it is demonstrated that a second-order convergence rate is achieved in practice.The proposed schemes are compared with the widely used explicit finite-difference schemes, such as Lax–Wendroff, Lax–Friedrichs and McCormack schemes. The benchmark problems for simulation of flows in the following vascular systems are considered: human carotid artery, human aorta with bifurcation, vessel with bifurcation and absorbing outflow conditions, and networks with 31, 63, and 127 vessels. It is demonstrated that the computations based on the splitting-based semi-implicit schemes can be performed much faster than for the explicit schemes with close values of relative errors.

A Novel Gabor Filtering and Adaptive Histogram Equalization Method for Improving Images

The correct information may only sometimes be effectively conveyed by images due to various factors, such as excessively bright or dark lighting and low or high contrast. As a result, picture improvement has become an essential part of digital image processing. This proposed method aims to develop an algorithm for improving photos captured in dark environments. This letter presents a new picture-enhancing approach that combines median and Gabor filtering using the wavelet domain with histogram equalization working over a spatial domain. The proposed method in this paper combines spatial and transformed domains for image enhancement and has been simulated using MATLAB. The simulation results of two different photos show that the suggested approach extends the histogram over a wide range of grayscale, offering a superior improvement to the original image. The novel proposed algorithm aims to improve image quality and visibility, making identifying essential details within the image easier. Further, the proposed technique's success is manifested by examining the produced photos' contrast and brightness. The findings reveal that the suggested technique beats the other strategies for improving low-contrast photos.

Event-Triggered Sliding-Mode Control for Fuzzy Semi-Markovian Jump Systems With Dead Zone Input and Application to Circuit Systems

This paper investigates event-triggered sliding mode control (SMC) for fuzzy semi-Markovian jump systems (S-MJSs) subject to limited communication capacity, in which we consider the network-induced transmission delay, dead-zone input and partially unknown transition rates of jumping mode in a whole framework. First, an event-triggered mechanism(ETM) is employed in the sensor-to-controller channel to save network resources efficiently. Then, a novel integral sliding surface is obtained with the aid of the event-triggered state information, and some new sufficient conditions on the stochastic stability of the overall closed-loop system are given in the sense of partially unknown transition rates via stochastic stability theory and matrix inequality method. Furthermore, a mode-depended sliding mode controller is designed to guarantee the reachability of the sliding surface in finite time by using the Lyapunov stability theory. Finally, a TD circuit model is provided to illustrate the validity of the proposed control scheme.

REGULARIZED JACOBI-TYPE ADMM-METHOD FOR FINDING SOLUTIONS TO GENERALIZED NASH EQUILIBRIUM PROBLEM

In this paper, we extended the well-known alternating direction method of multipliers (ADMM) for optimization problems to generalized Nash equilibrium problems (GNEP) with shared constraints. We developed an ADMM-type algorithm with fixed regularization to tackle the problem (GNEP) where an upper estimate for the operator norm is not known and then we apply a multiplier-penalty in order to get rid of the joint constraints. We equipped the Hilbert space with an appropriate weighted scalar product and it turns out to be weakly convergent under a lipschitz and monotonicity assumption. A proximal term is then added to improve the convergence properties. Furthermore, a comparative analysis of quasi-variational inequality method, interior point method, penalty method and the proposed method are discussed.

Non-fragile guaranteed cost control of microbial fuel cells

A microbial fuel cell (MFC), which is a new type of energy source, utilises electrogenic bacteria in sewage or soil to convert chemical energy into electrical energy. MFCs typically require an external controller to provide a stable output voltage to the external load. This study develops a non-fragile guaranteed cost (NFGC) controller to suppress the interference of the controller of an MFC and ensure that the quadratic cost function of the system satisfies certain performance indexes. First, for the convenience of controller design, a Takagi–Sugeno fuzzy model is established to approximate a single-chamber single-population MFC model. Subsequently, the linear matrix inequality method is used to design the NFGC controller. This control scheme can reduce the influence of controller disturbances on the system and ensure asymptotic stability of the closed-loop system under the specified upper bound of the provided cost function. The simulation results demonstrate that the developed control method has a shorter adjustment time and smaller steady-state error than traditional control methods such as sliding mode control (SMC), backstepping control, and fuzzy SMC

Estimating Public Transportation Accessibility in Metropolitan Areas: A Case Study and Comparative Analysis

Accessibility-oriented public transportation planning can improve the operational efficiency of public transportation, guide orderly urban development, and alleviate issues such as traffic congestion, environmental pollution, and resource consumption in large cities. To promote the practical application and widespread adoption of public transportation accessibility estimating systems, this study proposes an improved public transport accessibility levels (PTAL) method. It innovatively incorporates residents’ preference indices for different modes of transportation and addresses the challenge of missing timetable data in the calculation process. Using actual data from Shenzhen, a case study is conducted to analyze the public transportation accessibility index and compare the results obtained through k-means clustering, the equal spacing method, the quantile method, and the application of the London PTAL method. The research findings indicate that the optimal number of clusters for public transportation accessibility index analysis in large cities is six when using clustering algorithms. Among the statistical analysis methods, the quantile method shows favorable performance. Furthermore, a comprehensive comparison of different classification methods confirms that the improved PTAL method offers better discrimination in estimating public transportation accessibility levels compared to the London PTAL method. The study concludes by providing guidance on how cities with different characteristics can reference the improved PTAL method.

Mapping social vulnerability to floods. A comprehensive framework using a vulnerability index approach and PCA analysis

In recent years, the analysis of social vulnerability to floods became an integrated part of the flood risk management process, the strategies and policies developed focusing on risk reduction methods that increase the resilience of vulnerable communities. Therefore, reliable and robust social vulnerability approaches are needed, which is also highlighted by the increasing socio-economic growth and climate change related effects that can lead to unpredictable consequences. The use of vulnerability indices is the most widespread methodology that allows the identification of the most vulnerable communities and the understanding of the factors that increase vulnerability to floods. However, due to the lack of a standard procedure, the existing studies are often characterized by uncertainties related to subjective selection of indicators, lack of inclusion of all vulnerability dimensions, equal or subjective weighting methods, and reduced number of variables due to data unavailability. The present paper is addressing these gaps by developing a comprehensive methodology approach which: includes a large set of indicators that are selected considering the local socio-economic context, includes the hazard dimension in the analysis considering flood related variables, and applies objective weighting methods based on the Principal Component Analysis (PCA) method. Furthermore, flood vulnerability maps are developed using Geographic Information System (GIS) tools, to provide a rapid and easy way to identify highly vulnerable areas. The results showed that the integration of statistical methods and GIS tools in the social vulnerability index construction provides a better understanding of the factors that increase the social vulnerability and offers an overview of the mitigation and adaptation measures that must be implemented by authorities in order to improve the flood risk management.

Stability analysis of different control modes of grid-connected converters under different grid conditions

With the sustained popularity of renewable energy generation, high penetration of variable energies, e.g., wind and solar, is reshaping the form of power systems and weakening the strength of the grid. The stability mechanism of the grid-connected converter in a weak power network, however, has yet to be evaluated. This paper establishes impedance and transient models for Grid-Following (GFL) as well as Grid-Forming (GFM) converters through the impedance analysis method and equal area criterion analysis method. The stability of these two control methods is then comprehensively studied under small and large interference with different grid conditions. The analytical results show that the GFM control is more stable against small disturbances in a weak network. In contrast, it is prone to a significant disturbance stability problem in the strong grid due to the large grid impedance. The GFL control is more suitable for a vigorous power grid, whereas introducing oscillation in a weak power grid due to its negative damping. Simulation experiments have verified the accuracy of the analytical results.

Identify most promising temporal scour depth formula for circular piers proposed over last six decades

Temporal scour depth (dst) measurement at bridge piers is a significant issue for application engineers because accurate prediction is required for real-time monitoring. Although several formulas were employed, definitive study is still needed to determine which formula predicts most accurately. Therefore, here, a comparative analysis of dst prediction formulas, at circular piers in clearwater conditions, is performed using laboratory datasets for evolution over 57-years based on seven parameters: sediment coarseness, flow shallowness, constriction ratio, threshold-Froude number, sediment uniformity, relative density, time. Goal is also to select best option that will provide more useful compromise solution concept rather than examining ideal solutions based on inconsistencies. Therefore, useful statistical influence factors are used to perform 11 literature formulas with a newly derived formula in relation to their fitting coefficients. Weightage of each influencing factor is calculated using equal weightage and CRITIC methods. Ranking of each formula is determined using compromise programming. Overall study reveals that NCHRP (2011) formula is still most useful to determine dst if equilibrium scour depth (dse) and equilibrium time (te) are known. Melville and Chiew's (1999) formula is very close to NCHRP (2011). But without presuming dse and te, the newly proposed formula most accurately predicts the dst.

Robust dynamic output feedback predictive control for discrete uncertain systems with time-varying delays

A robust dynamic output feedback predictive control approach is developed for a discrete system with time-varying delays, unknown external disturbances, and unmeasurable states. First, the discrete system is transformed into an incremental state deviation model. Based on this model, a novel tracking deviation feedback model is established by extending the output tracking error. Then, a robust predictive control law, possessing more degrees of freedom, is designed. The closed-loop model is further given in conjunction with the feedback model. Second, by using the linear matrix inequality (LMI) method, relaxation technique, and variable transformation method, a less conservative stability condition is given in LMI form, which allows the controller to tolerate a greater range of time-varying delays. The gains of the control law are acquired by solving the stability condition, and the control performance can be significantly enhanced. Finally, by utilizing the TTS20 water tank as a simulation case, the viability and effectiveness of the proposed method are demonstrated.

A Singularly Perturbed Dirichlet Problem for a Ring With Singular Boundaries

Uniform asymptotic expansions of small-parameter solutions of bisingular Dirichlet problems for a ring with any degree of accuracy are constructed by a generalized method of boundary functions and a small-parameter method. The investigated problems have two features: equations with a small parameter in the first derivatives and external solutions simultaneously have increasing features on the boundaries of the domain, i. e. limit equations have singularities at the same time on both boundaries of the ring. Formal asymptotic expansions are based on the maxima principle and the method of differential inequalities. The resulting asymptotic series are represented by the Puiseux series. The main term of the asymptotic expansions of the solutions has a negative fractional power with respect to the small parameter.

Nonlinear error reduction for phase-shifting profilometry considering periodicity and symmetry of a phase histogram.

Phase-shifting profilometry is extensively utilized for three-dimensional (3D) measurement. However, because of gamma nonlinearity, the image intensities of the captured fringe patterns are regrettably distorted. An effective nonlinear error reduction method without requiring parameter estimation is presented in this paper. Differing from the traditional whole-period phase histogram equalization (PHE) method, our method takes into account not only the periodicity but also the symmetry of the phase histogram. Taking a three-step phase-shifting algorithm as an example, the phase error frequency triples the fringe frequency; thus, we first propose a 1/3-period PHE method. Moreover, since the phase error distribution is sinusoidal with symmetry, we further propose a 1/6-period PHE method. Simulations and experiments both indicate that the 1/6-period PHE method, compared with the whole-period PHE and 1/3-period PHE methods, can further reduce the nonlinear error.

Equivalent Conditions of Complete p-th Moment Convergence for Weighted Sum of ND Random Variables under Sublinear Expectation Space

We investigate the complete convergence for weighted sums of sequences of negative dependence (ND) random variables and p-th moment convergence for weighted sums of sequences of ND random variables under sublinear expectation space. Using moment inequality and truncation methods, we prove the equivalent conditions of complete convergence for weighted sums of sequences of ND random variables and p-th moment convergence for weighted sums of sequences of ND random variables under sublinear expectation space.

Sustainability Ranking of Turkish Universities with Different Weighting Approaches and the TOPSIS Method

The concept of sustainability has become more important, especially as a result of the depletion of energy resources and increasing environmental concerns. UI GreenMetric ranks universities based on sustainability, environmental, and energy concerns, addressing issues of environmental pollution, food and water scarcity, and energy supply. By prioritizing sustainability on their campuses and campuses, universities are working to ensure a more sustainable future for humanity. This study evaluates university sustainability in energy and climate change using the UI GreenMetric ranking, focusing on Turkish universities’ sustainability ranking. It incorporates variables like infrastructure, energy, climate change, waste, water, public transportation, and educational research, using weighting approaches to reveal the most important variables for the country’s universities. The study utilized weighting techniques like CRITIC, entropy, standard deviation-based, and equal weighting approaches to obtain rankings for UI GreenMetric rankings. Entropy and equal weighting methods were found to be closest to the UI GreenMetric rankings. Universities’ rankings were analyzed using the TOPSIS method and four weighting techniques for 83 Turkish universities. For Turkish universities, the CRITIC method yielded the highest weight for energy and climate change variables, while water was identified as the most significant factor for entropy, installation infrastructure, and standard deviation-based weighting techniques.

Local-in-time well-posedness theory for the inhomogeneous MHD boundary layer equations without resistivity in lower regular Sobolev space

In this paper, we consider the well-posedness theory in lower regular Sobolev space for the Prandtl-type equation arising from the inhomogeneous incompressible MHD equation in dimension two. Due to the degeneracy of horizontal dissipation, the well-posedness result of MHD boundary layer equation is established in higher regular Sobolev space rather than lower one generally. By using the anisotropic Sobolev inequality and characteristic line method, the well-posedness result of inhomogenous MHD Prandtl-type equation can be established under the H2−framework.

Cooperation of multi-task segmentation and a graph convolutional network for object vector boundary extraction in remote-sensing imagery

ABSTRACT Identifying and vectorizing the object in the image is an important part of producing high-precision vector maps. Deep learning can automatically extract vector boundaries accurately, but it still does not satisfy the application requirements for boundaries. Clearer boundaries and more concise vector points are also important components that cannot be neglected in vectorization. Taking buildings as the research object, we introduce a cooperative neural network of multi-task segmentation and graph convolution to improve the extraction of buildings by strengthening the boundaries and strategically selecting key points. We design a multi-task neural network to extract and optimize the vector boundaries, whose key points can be selected and refined with a graph convolutional network. In addition, to improve the coherence between features and jointly multi-information, we design a mutual-supervision loss for our method. Our experimental results show that our method effectively extracted buildings and outperformed several equal methods on the different public datasets.

Evaluating the Reactions of Bridge Foundations to Combined Wave–Flow Dynamics

As the ongoing development of national infrastructure progresses, we see an increase in the construction of deep-water bridges, specifically cross-sea bridges. This paper uses Stokes’s wave theory to simulate and analyze how a bridge foundation dynamically responds to wave–fluid interactions. Firstly, the governing equations, boundary conditions and initial conditions of fluid motion are derived, expanded and solved via Stokes’s wave theory, and a spectral model is simulated and plotted. Based on the P-M spectrum and equal frequency method, a method of wave height attenuation during wave propagation is proposed. Using an SSTK-ω turbulence model, a numerical wave flume is established considering the fluid model, the selection of element type and the boundary conditions set, and the influencing factors of wave propagation (attenuation) are analyzed. Waves with different wave parameters (period, depth and height) are numerically simulated and compared with the theoretical values. Finally, we perform an analysis of the dynamic response under wave–current coupling conditions. We establish different operational scenarios and obtain the following results: under a load duration of 200 s, the peak transverse displacements for spans 1, 2 and 3 measure at 0.84 m, 0.63 m and 0.62 m, respectively. The peak transverse displacements under operational scenarios 2 and 3 show reductions of 25.0% and 25.7%, respectively, when compared to scenario 1. However, large transverse displacements remain. This suggests that the influence of waves and water flow on the transverse displacement of the main span should not be overlooked.

Load frequency control of power system based on improved AFSA-PSO event-triggering scheme

Aiming at the impact of redundant information transmission on network resource utilization in current power systems, an improved event-triggered scheme based on particle swarm optimization and artificial fish swarm algorithm for power system load frequency control (LFC) with renewable energy is proposed. First of all, to keep the stability and security of power systems with renewable energy, the load frequency control scheme is investigated in this paper. Then, to relieve the communication burden and increase network utilization, an improved event-triggered scheme based on the particle swarm algorithm and artificial fish swarm algorithm is explored for the power system load frequency control. Then, by utilizing improved Lyapunov functional and the linear matrix inequality method, sufficient condition for the H∞ stability of the load frequency control system is established. Finally, a two-area load frequency control system and IEEE-39 node simulation models are constructed to verify the effectiveness and applicability of the proposed method.

Fuzzy Logic with Deep Learning for Detection of Skin Cancer

Melanoma is the deadliest type of cancerous cell, which is developed when melanocytes, melanin producing cell, starts its uncontrolled growth. If not detected and cured in its situ, it might decrease the chances of survival of patients. The diagnosis of a melanoma lesion is still a challenging task due to its visual similarities with benign lesions. In this paper, a fuzzy logic-based image segmentation along with a modified deep learning model is proposed for skin cancer detection. The highlight of the paper is its dermoscopic image enhancement using pre-processing techniques, infusion of mathematical logics, standard deviation methods, and the L-R fuzzy defuzzification method to enhance the results of segmentation. These pre-processing steps are developed to improve the visibility of lesion by removing artefacts such as hair follicles, dermoscopic scales, etc. Thereafter, the image is enhanced by histogram equalization method, and it is segmented by proposed method prior to performing the detection phase. The modified model employs a deep neural network algorithm, You Look Only Once (YOLO), which is established on the application of Deep convolutional neural network (DCNN) for detection of melanoma lesion from digital and dermoscopic lesion images. The YOLO model is composed of a series of DCNN layers we have added more depth by adding convolutional layer and residual connections. Moreover, we have introduced feature concatenation at different layers which combines multi-scale features. Our experimental results confirm that YOLO provides a better accuracy score and is faster than most of the pre-existing classifiers. The classifier is trained with 2000 and 8695 dermoscopic images from ISIC 2017 and ISIC 2018 datasets, whereas PH2 datasets along with both the previously mentioned datasets are used for testing the proposed algorithm.