Proposed Hybrid Method Research Articles

Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach

Accurate and effective prediction of polycyclic aromatic hydrocarbons (PAHs) in surface water remains a substantial challenge due to the limited understanding of the dynamic processes. To assist integrated surface water management, a novel hybrid surface water PAH prediction model based on a two-stage decomposition approach and deep learning algorithm was proposed. Specifically, a two-stage decomposition technique consisting of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) was first introduced to decompose the data into several subsequences to extract the main fluctuations and trends of the PAH sequence. Subsequently, the deep learning algorithm long short-term memory (LSTM) was employed to explore the latent dynamic characteristics of each subsequence. Finally, the predicted values of the subsequences were integrated to obtain the final predicted results. An empirical study was conducted based on PAH data of eight major rivers in Saxony, Germany. The empirical results proved that the CEEMDAN-VMD-LSTM model outperformed other benchmark data-driven methods in predicting PAHs in surface water because it combined the advantages of two-stage decomposition and deep learning methods. The mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2) of the model were 27.89, 37.92 and 0.85, respectively. The proposed hybrid method can achieve effective and accurate water quality prediction and is an effective tool for surface water management.

Speckle filtering of ultrasonic images using weighted nuclear norm minimization in wavelet domain

Medical images such as ultrasound images suffer from multiplicative speckle noise which reduces the contrast of ultrasound images and adversely affects the diagnosis process. Discrete Wavelet transform (DWT) is widely accepted in the area of image processing due to its time localization, multi-resolution and sparseness properties. DWT decomposes the noisy image into approximation coefficients and detail coefficients. The detail wavelet coefficients of clean image have sparsity property, whereas in the case of noisy image, sparsity of detail coefficients is reduced due to the presence of speckle noise. Thus, in the proposed method, low rank based Weighted Nuclear Norm Minimization (WNNM) is applied on detail coefficients to reveal the sparsity property of DWT. WNNM is applied on the group matrix of non-local similar patches of detail subbands of DWT to approximate the low-rank denoised version of the subbands. Moreover, less amount of edge and structure information is present in the approximation coefficients of DWT. Thus, in the proposed method, Non-local Means (NLM) filter with Square-Chord distance is also used to denoise speckle noise from approximation coefficients of DWT. Exhaustive experiments are conducted on various images such as real US images, simulated kidney image and synthetic image. It is observed that the mean improvement in terms of PSNR and CNR values of the proposed hybrid method are 1.50% and 3.81% respectively over WNNM based Despeckling using Low-Rank Approximation (DLRA) method. Qualitative and quantitative analyses show that the proposed hybrid method performed better than existing speckle reduction methods in terms of both speckle reduction and edge preservation.

An efficient approach based power flow management in smart grid system with hybrid renewable energy sources

This manuscript proposes an efficient hybrid method for power flow management (PFM) in smart grid (SG) system with hybrid renewable energy sources (HRES), such as photovoltaic (PV), wind turbine (WT), micro turbine (MT), fuel cell (FC), diesel generator (DG), battery energy storage (BES). The proposed hybrid method is the consolidation of turbulent flow of water-based optimization (TFWO) and battle royale optimization (BRO), hence it is called TFW-BRO. The major objective of the proposed approach is “to cost reduction and control the power flow (PF) of the system. The control signal of the voltage source inverter is generated by TFWO depending on the variation of power transmission between the source and load side. The control signal is assured using BRO via the parallel implementation against active with reactive power type. The power balance constraints, capacity constraints are used for define the objective function. Depending on the variation of PF, the TFW-BRO method optimizes the control parameter of the controller. Finally, the proposed method is activated in Matrix Laboratory (MATLAB)/Simulink site, and then the performance is likened with other existing methods. The statistical analysis such as mean, median, standard deviation for the proposed technique is 0.9681, 0.9062 and 0.1099 respectively.

Well-balanced central schemes for pollutants transport in shallow water equations

We present a new one-dimensional hybrid second-order accurate well-balanced unstaggered central scheme/particle method (WBUCS-P) for computing the transport of pollutant in water flows. The shallow water equations are employed to model the free-surface water flow, and the transport equation is employed to model pollutant propagation. Our proposed scheme employs the particle method and avoids the resolution of Riemann problems occurring at the boundaries of neighboring cells. Furthermore, the proposed hybrid method is second-order accurate both in space and time; it evolves a piecewise linear numerical solution on a single grid, and benefits from the non-dissipativeness property of the particle method. Our scheme is also well-balanced due to a unique integration of the source term and to the application of the surface gradient method. We implement the proposed scheme and solve some selected problems featuring the transport of pollutants. Accurate pollutant’s location and concentration are observed and reported in the numerical test cases.

Combining deep learning with geometric features for image-based localization in the Gastrointestinal tract

Tracking monocular colonoscope in the GastroIntestinal (GI) tract is challenging as the obtained images suffer from deformation, blurred textures, and significant changes in appearance. These drawbacks greatly restrict the tracking ability of conventional geometry-based methods, which are heavily dependent on the performance of corner points extraction from the image. Even though end-to-end Deep Learning (DL) can overcome these issues, limited labeling data is a roadblock to the state-of-the-art DL-based method. To handle these drawbacks, we propose a novel approach to combine the DL-based method with the traditional geometry-based approach to achieve better localization with small training data. In this work, a DL network is trained with the images of the pre-operative endoscopy/colonoscopy. Siamese architecture is introduced to perform the zone labeling of the image based on the anatomical segmentation with expert knowledge. Then, using the image in the therapeutic intervention, our method predicts the 6 degrees of freedom scope pose and recover geometric reference to the images from the pre-operative endoscopy/colonoscopy. The DL network predicts the zone of the testing image, and the pre-generated triangulated map points within the zone in the training set are registered with the bundle adjustment algorithm. The proposed hybrid method is tested on the synthetic data sets and the real-world in-vivo data sets. Further, the results achieved through various experiments validate that the proposed method outperforms traditional geometry-based only or DL-based only localization techniques.

A CNN-based hybrid model to detect Coronavirus disease

In this paper, a hybrid classification technique for COVID-19 disease is proposed. The proposed model solves the two-class classification problem (covid, normal). In this study, we have presented hybrid models integrating superior deep learning and machine learning classifiers: Convolutional Neural Network (CNN) and Support Vector Machine (SVM), CNN and AdaBoost, CNN and K Nearest Neighborhood (kNN), CNN and Multilayer Perceptron (MLP), CNN and Naive Bayes (NB). In these models, CNN performs as a trainable deep feature extractor, and SVM, AdaBoost, kNN, MLP, NB behave as a recognizer. All experiments have been performed on COVID-CT and SARS-CoV-2 CT combined image datasets. As a result, proposed hybrid methods have been compared in terms of sensitivity, accuracy, precision, F1-score, AUC-score, specificity, FPR, FDR, and FNR. CNN+SVM, CNN+MLP, and CNN+kNN have achieved outperforming results according to the other models, respectively. Also, CNN+SVM performed the best (achieving 85.85% sensitivity, 85.86% precision, 85.86% accuracy, 85.85% F1-score, 85.85% AUC score, 86.47% specificity, 13.52% FPR, 13.86% FDR, and 14.76% FNR). When the results are examined, the proposed hybrid system is seen to be efficient to detect COVID-19. Also, the performance of the proposed hybrid system is better than the successful studies found on COVID-CT and SARS-CoV-2 CT combined image datasets in the literature.

A hybrid ensemble-filter wrapper feature selection approach for medical data classification

Background and objectiveMedical data plays a decisive role in disease diagnosis. The classification accuracy of high-dimensional datasets is often diminished by several redundant and irrelevant features. In this context, feature selection becomes an indispensable process. Feature selection primarily intends to identify a feature subspace which retains the classification accuracy while reducing the high computational cost of learning model as well as eliminating noise. The suitability of an appropriate feature selection approach heavily depends upon the capability of that approach to match the problem framework and to discover the intrinsic patterns within the data. The prime objective of this paper is to develop an ensemble-filter based hybrid feature selection model for disease detection. MethodsIn this paper, a four-step hybrid ensemble feature selection algorithm has been introduced. Firstly, the dataset is partitioned using the cross-validation procedure. Secondly, in the filter step, various filter methods based on weighted scores were ensembled to generate a ranking of features, and thirdly sequential forward selection algorithm is utilized as a wrapper technique to obtain an optimal subset of features. Finally, the resulting optimal subset is processed for subsequent classification tasks. ResultsExperiments have been performed on twenty benchmark medical datasets with different dimensionalities. The performance of the proposed hybrid approach is compared with fourteen state-of-the-art feature selection algorithms on four benchmark classifiers namely, Naïve Bayes, Support Vector Machine with Radial Basis Function, Random Forest and k-Nearest Neighbor. The empirical results clearly demonstrate the superiority of the proposed hybrid methodology over the competing methods with respect to accuracy, sensitivity, specificity, f1-score, area under curve evaluation measures and number of selected features. The statistical analysis of the obtained results shows the outperformance and the competitiveness of the proposed hybrid method with respect to various state-of-the-art algorithms. ConclusionsThis study concludes that the proposed hybrid approach proves to be more effective and reliable feature selection technique in selecting highly discriminative features. The framework can be utilized as a promising tool by both clinicians and researchers in enhancing the classification performance of medical datasets.

A new hybrid method for reducing cogging torque in the AFPM wind generator

ABSTRACT Reducing the cogging torque in the axial flux permanent magnet (AFPM) generators is an important study area. Especially, AFPM generators with open slotted and rectangular-shaped poles have high cogging torque and high total harmonic distortion (THD). Generators used in wind turbines require minimal mechanical vibration and a smooth sinusoidal wave. In this study, a new hybrid method is proposed to reduce the cogging torque and increase electrical performance. The proposed method involves combining two techniques to reduce cogging torque, including magnet placement angle and magnet grouping technique. In the proposed hybrid method, the electrical performance of the AFPM generator has improved for the minimum cogging torque. This proposed new method is cost-effective and easy to apply. 3D finite element method (FEM) and experimental verifications were performed for the accuracy of the proposed method. When the results are examined, cogging torque was improved by at least 80% with the new hybrid method. However, the electrical performance has been improved for 10% power loss, and the total harmonic distortion (THD) has been reduced to less than 3%.

A Hybrid Multiple Criteria Decision-Making Technique to Evaluate Regional Intellectual Capital: Evidence from China

With the dawn of economic globalization and the knowledge economy, intellectual capital has become the most important factor to determine economic growth. However, due to resource endowment, location conditions, policy differences, and other factors, provinces in China show sizeable differences in regional intellectual capital (RIC), which affects the coordinated development of the regional economy. Evaluating RIC is a typical multiple-criteria decision-making (MCDM) problem. Therefore, this study employs a set of MCDM techniques to solve this problem. First, the Delphi method is used to determine the formal decision structure based on a systematic literature review. A novel hybrid method, namely, the Grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP), i.e., GDANP, is employed to obtain the relative weight of each criterion. Finally, based on the data of 31 provinces in China, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is used to evaluate the RIC. According to the questionnaires filled out by an expert panel, we establish an evaluation index of RIC with 21 criteria. Based on the results of empirical study, the level of RIC in different regions in China is quite different. Furthermore, the RIC ranking is largely consistent with the provincial gross domestic product (GDP) ranking, in line with the current status of development in the regions. Indeed, this paper shows that the proposed hybrid method can effectively measure the level of RIC.

Stochastic Hybrid Event Based and Continuous Approach to Derive Flood Frequency Curve

This study proposes a methodology that combines the advantages of the event-based and continuous models, for the derivation of the maximum flow and maximum hydrograph volume frequency curves, by combining a stochastic continuous weather generator (the advanced weather generator, abbreviated as AWE-GEN) with a fully distributed physically based hydrological model (the TIN-based real-time integrated basin simulator, abbreviated as tRIBS) that runs both event-based and continuous simulation. The methodology is applied to Peacheater Creek, a 64 km2 basin located in Oklahoma, United States. First, a continuous set of 5000 years’ hourly weather forcing series is generated using the stochastic weather generator AWE-GEN. Second, a hydrological continuous simulation of 50 years of the climate series is generated with the hydrological model tRIBS. Simultaneously, the separation of storm events is performed by applying the exponential method to the 5000- and 50-years climate series. From the continuous simulation of 50 years, the mean soil moisture in the top 10 cm (MSM10) of the soil layer of the basin at an hourly time step is extracted. Afterwards, from the times series of hourly MSM10, the values associated to all the storm events within the 50 years of hourly weather series are extracted. Therefore, each storm event has an initial soil moisture value associated (MSM10Event). Thus, the probability distribution of MSM10Event for each month of the year is obtained. Third, the five major events of each of the 5000 years in terms of total depth are simulated in an event-based framework in tRIBS, assigning an initial moisture state value for the basin using a Monte Carlo framework. Finally, the maximum annual hydrographs are obtained in terms of maximum peak-flow and volume, and the associated frequency curves are derived. To validate the method, the results obtained by the hybrid method are compared to those obtained by deriving the flood frequency curves from the continuous simulation of 5000 years, analyzing the maximum annual peak-flow and maximum annual volume, and the dependence between the peak-flow and volume. Independence between rainfall events and prior hydrological soil moisture conditions has been proved. The proposed hybrid method can reproduce the univariate flood frequency curves with a good agreement to those obtained by the continuous simulation. The maximum annual peak-flow frequency curve is obtained with a Nash–Sutcliffe coefficient of 0.98, whereas the maximum annual volume frequency curve is obtained with a Nash–Sutcliffe value of 0.97. The proposed hybrid method permits to generate hydrological forcing by using a fully distributed physically based model but reducing the computation times on the order from months to hours.

Solving the pan evaporation process complexity using the development of multiple mode of neurocomputing models

Finding an accurate computational method for estimating pan evaporation (EPm) can be useful in the application of these methods for the development of sustainable agricultural systems and water resources management. In the present study, the proposed hybrid method called multiple model-support vector machine (MM-SVM) with the aim of showing the increasing, decreasing, and constant accuracy behavior of this hybrid model and improving the results of estimating EP compared to the two models ANN and SVM on a monthly scale of EPm in four meteorological stations (Ardabil, Khalkhal, Manjil (from Iran), and Grand Island (from the USA)) located in semi-arid regions, using the output of artificial intelligence (AI) models (i.e., artificial neural network (ANN) and support vector machine (SVM)), was evaluated. The results of intelligent models using several statistical indices (i.e., root mean square error (RMSE), mean absolute error value (MAE), Kling-Gupta (KGE), and coefficient of determination (R2)) and with the help of case visual indicators were compared. According to the results of evaluation indicators in the test phase, MM-SVM-6, ANN-5, MM-SVM-3, and MM-SVM-7 with RMSE = 1.088, 0.761, 0.829, and 0.134 mm/day; MAE = 0.79, 0.54, 0.589, and 0.105 mm/day; KGE = 0.819, 0.903, 0.972, and 0.981; and R2 = 0.939, 0.962, 0.967, and 0.996 and with four input variables were introduced as the best models in Ardabil, Khalkhal, Manjil, and Grand Island stations, respectively. The proposed hybrid model (MM-SVM) was able to use its multi-model strategy with inputs estimated by independent models, its power to estimate EPm in scenarios where there is a high correlation between its components with EPm, in a feasible state Accept to show. So that the incremental, constant, and decreasing modes in EPm estimation accuracy by this hybrid model under the semi-arid climatic conditions of the studied areas were quite clear. Therefore, the results of the proposed and superior models in the present study can help local stakeholders in discussing water resources management.

HYIMFO: Hybrid method for optimizing fiber orientation angles in laminated piezocomposite actuators

Numerous engineering applications employ composite materials due to the advantages that the combination of their components can provide. Fiber-reinforced composites are a particular group of composites which allows designing optimized structures by tailoring the orientation of the fiber. For this reason, new additive manufacturing technologies have been developed nowadays. These tools allow the manufacturing of fiber-reinforced structures designed by using optimization algorithms. Consequently, there are several works in recent literature determining the optimized fiber orientation in composite structures, where the methods based on gradient stand out due to their better efficiency. There are two main approaches to determine the fiber orientation by using gradient-based methods, Continuous Fiber Angle Optimization (CFAO), where the angle is the design variable and commonly present local minima problems, and Discrete Fiber Angle Interpolation Models, which consider candidate angles a priori and by using weighting functions choose one of them as the optimized angle. This work proposes a method named HYMFO, which uses a combination of both approaches. To verify the efficiency of the method, two design problems of Laminated Piezocomposite Actuators (LAPA) are solved by using several mesh discretizations with the discrete methods DMO, HPDMO, BCP, and NDFO, the continuous method SPIMFO and the newly proposed hybrid method. Therefore, the method is not evaluated with the traditional problem of maximizing the stiffness in structural design. A new fiber orthogonality regularization is presented, facilitating the appearance of continuous paths in fiber distribution by imposing a specified maximum angle between adjacent fibers. A comparative analysis based on the performance of the structure and the computational cost is carried out to determine the most appropriate method for each situation.

Classification of Remote Sensing Image Scenes Using Double Feature Extraction Hybrid Deep Learning Approach

Over the last decade, remote sensing technology has advanced dramatically, resulting in significant improvements on image quality, data volume, and application usage. These images have essential applications since they can help with quick and easy interpretation. Many standard detection algorithms fail to accurately categorize a scene from a remote sensing image recorded from the earth. A method that uses bilinear convolution neural networks to produce a lessweighted set of models those results in better visual recognition in remote sensing images using fine-grained techniques. This proposed hybrid method is utilized to extract scene feature information in two times from remote sensing images for improved recognition. In layman's terms, these features are defined as raw, and only have a single defined frame, so they will allow basic recognition from remote sensing images. This research work has proposed a double feature extraction hybrid deep learning approach to classify remotely sensed image scenes based on feature abstraction techniques. Also, the proposed algorithm is applied to feature values in order to convert them to feature vectors that have pure black and white values after many product operations. The next stage is pooling and normalization, which occurs after the CNN feature extraction process has changed. This research work has developed a novel hybrid framework method that has a better level of accuracy and recognition rate than any prior model.

A Novel Computational Intelligence Approach for Coal Consumption Forecasting in Iran

Coal as a fossil and non-renewable fuel is one of the most valuable energy minerals in the world with the largest volume reserves. Artificial neural networks (ANN), despite being one of the highest breakthroughs in the field of computational intelligence, has some significant disadvantages, such as slow training, susceptibility to falling into a local optimal points, sensitivity of initial weights, and bias. To overcome these shortcomings, this study presents an improved ANN structure, that is optimized by a proposed hybrid method. The aim of this study is to propose a novel hybrid method for predicting coal consumption in Iran based on socio-economic variables using the bat and grey wolf optimization algorithm with an artificial neural network (BGWAN). For this purpose, data from 1981 to 2019 have been used for modelling and testing the method. The available data are partly used to find the optimal or near-optimal values of the weighting parameters (1980–2014) and partly to test the model (2015–2019). The performance of the BGWAN is evaluated by mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), standard deviation error (STD), and correlation coefficient (R^2) between the output of the method and the actual dataset. The result of this study showed that BGWAN performance was excellent and proved its efficiency as a useful and reliable tool for monitoring coal consumption or energy demand in Iran.

Human Reliability Analysis for Cardiopulmonary Resuscitation Process in Emergency Medicine Using a Modified Hybrid Method Based on the Markov Model and Fault Tree Analysis

Introduction:In emergency departments (ED), human reliability assessment is essential for improving the quality of treatment and preventing medical accidents. A medical accident is expressed as an injury to a patient caused by the negligence of a doctor or nurse who is providing medical care. This study aimed to assess the human reliability in the cardiopulmonary resuscitation (CPR) process and recommend some comments to minimize human errors and improve patient safety.Materials and Methods: The main factors in the CPR process (such as rate and depth of chest compression and rate of ventilation) are identified based on the American heart association (AHA) roles. Data were recorded during three months in the evening shifts in the ED and CPR room of Imam Reza Hospital in Mashhad, Iran. In total, 42 samples were collected, and a modified hybrid approach according to the fault tree analysis and Markov method was proposed for the analysis of CPR team (including emergency medicine, medical interns, and nurses) reliability in the resuscitation process. Finally, the important basic events (errors) were selected using the Boruta algorithm by R software.Results: An FTA-Markov-based hybrid method is considered to compute the human reliability in the CPR process. The obtained results from human reliability analysis using the sensitivity analysis via Boruta algorithm and the proposed hybrid method show that an interrupt between chest compression process for rhythm control, the cycle of CPR, the depth of chest compression, and the discussion about reversible causes are the most effective factors in the human reliability of CPR process.Conclusion:The human reliability of the CPR process in the ED has been assessed using a hybrid method based on the FTA and Markov method for the first time. To improve the quality of treatment and prevent medical accidents during the CPR process, the main factors in the process are identified, and then, the proposed hybrid method is used to calculate human reliability.

System modeling of micro‐grid with hybrid energy sources for optimal energy management—A hybrid elephant herding optimization algorithm‐adaptive neuro fuzzy inference system approach

AbstractThis manuscript proposes a hybrid method for system modeling and optimal allocation of low cost micro‐grid. The proposed hybrid method is the joint execution of the enhanced elephant herding optimization algorithm (EHOA) and adaptive neuro fuzzy inference system (ANFIS) named as EHO‐ANFIS. By using MG inputs, such as solar photovoltaic, wind turbine (WT), micro turbine (MT), fuel cell (FC), and battery energy storage system. EHO optimizes micro‐grid configuration in minimal fuel costs based on needed load requirement. Here, learning phase of ANFIS is utilized for predicting the load requirement. EHOA reduces operation and maintenance costs, emission cost on the basis of the predicted load requirement. The proposed method is executed in MATLAB/Simulink site and the robustness of the proposed method is compared with different existing methods. In the proposed method, the maximal generated power of photovoltaic represents 6 kW, wind turbine indicates 7.8 kW, micro turbine denotes 11.8 kW, FC implies 6.8 kW, and battery refers 3 kW. By utilizing genetic algorithm, the generated power of photovoltaic signifies 7 kW, WT implicates 6 kW, MT implicates 4 kW, FC refers 7 kW, and battery implies 14 kW. The proposed method has minimal cost effective depending on its load demand. The computational time of the proposed technique under 100, 250, 500, and 1000 trails is 5600, 14 000, 28 000, and 56 000 s.

Estimation of longitudinal excitation of propeller using a novel hybrid method

Due to the limitation of working environment and structure conditions, it is difficult to directly measure the propeller excitation of ship propulsion shafting system. A method based on waveguide function and modal method combining with shafting vibration response measurement was proposed to estimate the propeller longitudinal excitation indirectly. Firstly, a waveguide transfer model for the general structure of the propulsion shafting is proposed that is suitable for estimating propeller excitation from shafting vibration response. Secondly, the longitudinal excitation of the propeller is calculated by measuring the waveguide coefficients of any shaft sections via the proposed hybrid method. Finally, the scheme is verified by a concrete example. The simulation results prove its feasibility and effectiveness for estimating propeller excitation by measuring the shafting vibration response, which provides a novel scheme for accurately grasping the propeller excitation and effectively controlling the vibration and noise of hull structure.

Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach

In general, several conservative techniques are available for detecting cracks in concrete bridges but they have significant limitations, including low accuracy and efficiency. Due to the expansion of the neural network method, the performance of digital image processing based crack identification has recently diminished. Many single classifier approaches are used to detect the cracks with high accuracy. The classifiers are not concentrating on random fluctuation in the training dataset and also it reflects in the final output as an over-fitting phenomenon. Though this model contains many parameters to justify the training data, it fails in the residual variation. These residual variations are frequent in UAV recorded photos as well as many camera images. To reduce this challenge, a noise reduction technique is utilized along with an SVM classifier to reduce classification error. The proposed technique is more resourceful by performing classification via SVM approach, and further the feature extraction and network training has been implemented by using the CNN method. The captured digital images are processed by incorporating the bending test through reinforced concrete beams. Moreover, the proposed method is determining the widths of the crack by employing binary conversion in the captured images. The proposed model outperforms conservative techniques, single type classifiers, and image segmentation type process methods in terms of accuracy. The obtained results have proved that, the proposed hybrid method is more accurate and suitable for crack detection in concrete bridges especially in the unmanned environment.

Efficient method for real-time range enhancement of electro-optical imaging system

Electro-optical (EO) imaging sensors are employed for object detection, recognition, identification, and tracking. The observation range of EO systems is defined as the maximum distance at which a target of interest can be seen and perceived. The performance of these systems deteriorates under degraded weather conditions, which severely affects the image quality and thus the observation range of EO surveillance systems. An improved image quality results in an enhanced observation range. Image quality can be improved by applying suitable digital image enhancement techniques by considering target/background signature knowledge. Under bad weather conditions, these digital techniques become ineffective due to the presence of scattered background signals. To reduce the effect of scattering, optical methods are also used to mitigate degradation caused by the atmosphere. It is observed that the optical method alone cannot produce the desired result. Observation range enhancement in surveillance system is achieved using the proposed hybrid method, which utilizes the optimal combination of optical and digital techniques in real-time. The proposed method not only improves human visual perception but also reveals more image content for better image analysis. The performance of the proposed range enhancement technique is compared with the predicted observation range model. The processed images using the hybrid method show more than 30% observation range enhancement under bad weather conditions. In addition to the subjective evaluation, qualitative and quantitative evaluations of image quality achieved through the proposed method are also presented. It is observed that the proposed hybrid method significantly improves the observation range and image quality of EO sensors under degraded weather in real time.

Hybrid technique for load frequency control of renewable energy sources with unified power flow controller and energy storage integration

In this manuscript, the load frequency control (LFC) of multiple area hybrid power system (PS) integrated with renewable energy sources (RESs) using energy storage system (ESS) is proposed. The proposed hybrid method is the joint execution of Meta-heuristic Anopheles Search Algorithm (MASA) and artificial intelligence (AI) technique and hence it is named as MASAAI technique. The main aim of the proposal is “to diminish the load frequency deviations in 3-area hybrid PS.” The thyristor controlled phase shifter (TCPS) and ultra-capacitor (UC) application are used in LFC. During the operation of power system, the power distribution quality is suffered owing to continual and random changes in load. Hence, the PS control is necessary for maintaining a continual balance amid the power generation and load demand. The proportional integral derivative (PID) is agreed to LFC mode. Here, the 3-area system is incorporated with conventional RESs like solar, biomass, and fuel cell. UPFC is joined in series with tie-line, ensures the stabilization of the system and also presents more service and the quality of power is sustained by the subordinate control of super capacitor energy storage. With the help of MASAAI method, suppressing the peak value of the frequency deviation by power modulation control is a way to lessen this issue. With this proper control, the MASAAI method efficiency becomes optimum in deregulated environment that is agreed to LFC mode. Finally, the proposed method is activated in MATLAB/Simulink site and the performance is compared with existing methods. Finally, the simulation outcomes demonstrate that the efficiency of MASAAI method is more efficient compared to the existing methods.