Developed Hybrid Model Research Articles

Classification of Electrocardiogram Signals Based on Hybrid Deep Learning Models

According to the analysis of the World Health Organization (WHO), the diagnosis and treatment of heart diseases is the most difficult task. Several algorithms for the classification of arrhythmic heartbeats from electrocardiogram (ECG) signals have been developed over the past few decades, using computer-aided diagnosis systems. Deep learning architecture adaption is a recent effective advancement of deep learning techniques in the field of artificial intelligence. In this study, we developed a new deep convolutional neural network (CNN) and bidirectional long-term short-term memory network (BLSTM) model to automatically classify ECG heartbeats into five different groups based on the ANSI-AAMI standard. End-to-end learning (feature extraction and classification work together) is done in this hybrid model without extracting manual features. The experiment is performed on the publicly accessible PhysioNet MIT-BIH arrhythmia database, and the findings are compared with results from the other two hybrid deep learning models, which are a combination of CNN and LSTM and CNN and Gated Recurrent Unit (GRU). The performance of the model is also compared with existing works cited in the literature. Using the SMOTE approach, this database was artificially oversampled to address the class imbalance problem. This new hybrid model was trained on the oversampled ECG database and validated using tenfold cross-validation on the actual test dataset. According to experimental observations, the developed hybrid model outperforms in terms of recall, precision, accuracy and F-score performance of the hybrid model are 94.36%, 89.4%, 98.36% and 91.67%, respectively, which is better than the existing methods.

A newly developed hybrid method on pavement maintenance and rehabilitation optimization applying Whale Optimization Algorithm and random forest regression

ABSTRACT Developing an accurate pavement prediction model plays a dominant role in pavement M&R optimization. Despite employing different robust machine learning techniques to predict pavement conditions, these methods have some weaknesses in synchronising with exact optimization algorithms. The main contribution of this study is to propose a novel method for optimizing the pavement M&R plan with high accuracy. Contrary to conventional approaches, a robust prediction algorithm, Random Forest Regression (RFR), is applied to predict the pavement International Roughness Index (IRI). In addition, Multiple Linear Regression (MLR) is employed to assess the performance of the proposed technique in terms of IRI prediction accuracy. Whale Optimization Algorithm (WOA), as a powerful metaheuristic optimization algorithm, is utilised to obtain the optimal solution to the pavement M&R optimization problem. RFR is run as an internal part of the WOA in the introduced method. Furthermore, Genetic Algorithm (GA) is used to examine the performance of the proposed approach in finding the optimal solution. The RFR results conclude a more accurate prediction of IRI than MLR based on all machine learning performance indicators. Furthermore, the newly developed hybrid model significantly outperforms GA in finding the optimal and cost-effective solution to the M&R optimization problem.

Multi‐rate observer design and optimal control to maximize productivity of an industry‐scale fermentation process

AbstractChemical industries focus primarily on profitable operations, resulting in growing attention and advances in the field of digital twins and optimal control algorithms. However, most industries still struggle due to a lack of physical sensors, infrequent measurements, and asynchronous sampling. Thus, in this work, we have designed a multi‐rate state observer for state estimation from plant measurements and developed a model predictive controller (MPC) that maximized the profitability of an industry‐scale fermentation process (fermenter volume < 378,500 L). Additionally, as the fermentation process is complex due to the use of microorganisms, which cannot be accurately captured using a first‐principles model, we utilize a previously developed hybrid model in the proposed MPC formulation. The MPC uses a GAMS‐MATLAB framework to determine the optimal input profiles while considering practical process constraints. It is shown using multiple datasets, that the MPC can increase productivity while also decreasing the plant operating cost.

Non-Intrusive Load Monitoring of Residential Loads via Laplacian Eigenmaps and Hybrid Deep Learning Procedures

Today, introducing useful and practical solutions to residential load disaggregation as subsets of energy management has created numerous challenges. In this study, an intelligence hybrid solution based on manifold learning and deep learning applications is presented. The proposed solution presents a combined structure of Laplacian eigenmaps (LE), a convolutional neural network (CNN), and a recurrent neural network (RNN), called LE-CRNN. In the proposed model architecture, LE, with its high ability in dimensional reduction, transfers the salient features and specific values of power consumption curves (PCCs) of household electrical appliances (HEAs) to a low-dimensional space. Then, the combined model of CRNN significantly improves the structure of CNN in fully connected layers so that the process of identification and separation of the HEA type can be performed without overfitting problems and with very high accuracy. In order to implement the suggested model, two real-world databases have been used. In a separate scenario, a conventional CNN is applied to the data for comparing the performance of the suggested model with the CNN. The designed networks are trained and validated using the PCCs of HEAs. Then, the whole energy consumption of the building obtained from the smart meter is used for load disaggregation. The trained networks, which contain features extracted from PCCs of HEAs, prove that they can disaggregate the total power consumption for houses intended for the Reference Energy Disaggregation Data Set (REDD) and Almanac of Minutely Power Dataset (AMPds) with average accuracies (Acc) of 97.59% and 97.03%, respectively. Finally, in order to show the accuracy of the developed hybrid model, the obtained results in this study are compared with the results of similar works for the same datasets.

Remote Sensing Monitoring of Rice and Wheat Canopy Nitrogen: A Review

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality.

A reliable hybrid outlier robust non-tuned rapid machine learning model for multi-step ahead flood forecasting in Quebec, Canada

Reliable and accurate flood forecasting is a complex and challenging problem that is essential for the creation of disaster preparedness plans to protect life and reduce economic losses. This study suggests a novel hybrid model for real-time multi-steps-ahead flood forecasting within the Saint-Lawrence River in Quebec by integrating the discrete wavelet transform (DWT) and improved outlier-robust extreme learning machine (IORELM) models. The DWTIORELM model was constructed with three main novelties: (i) Consider the sparsity characteristics of the outliers to enhance the performance of the ELM-based model in the presence of outliers, (ii) Introduce an iterative process to overcome the random definition of the two main matrices in the ELM-based approaches, (iii) Integrate IORELM with DWT as a preprocessing technique to decompose the main input signals to high-scale and low-frequency signals. The performance of the individual IORELM is compared with the original version of ELM, Regularized ELM (RELM), and Weighted RELM (WRELM) in terms of global accuracy and accuracy in peak flow forecasting. The results indicated that the IORELM outperformed other models. However, there was a need for a model that could perform better for peak flow detection. IORELM, as the best individual model, was integrated with DWT, and the DWTIORELM model was developed to overcome this limitation. In addition, the comparison results of the hybrid DWTIORELM and individual IORELM models in terms of accuracy and simplicity as well as peak flow forecasting indicate that the introduced hybrid model (R = 0.99, NSE = 0.99; RMSE = 0.234; NRMSE = 0.021; MARE = 0.02) in the current study not only overcomes all limitations of the existing ELM-based methods but also could be applied in multi-steps-ahead flood forecasting. The optimum values of the number of hidden neurons, decomposition level, and iteration number were found to be 200, 1, and 500, respectively. Furthermore, the Sigmoid function and Discrete Meyer (DM) were selected as the best activation function and mother wavelet, respectively. The developed hybrid model was applied to check its performance in multi-steps-ahead flood forecasting for one to nine hours ahead. The results showed that it could forecast peak flows with an average relative error of less than 5 %.

Development of entropy embedded COPRAS-ARAS hybrid MCDM model for optimizing EDM parameters while machining high carbon chromium steel plate

The purpose of this paper is to discuss the optimization of electrical discharge machining (EDM) parameters in a green production environment. The primary goal of this work is to create a novel hybrid multi-criteria decision making (MCDM) model that combines COPRAS and ARAS to determine the best input EDM parameters for machining high carbon chromium tool steel plate. To carry out the selection process, a total of nine tests were carried out using four input parameters, including dielectric level, peak current, flushing pressure, and pulse duration at three different levels of magnitude, and the readings of the respective five output parameters were measured. The weights of these five criterions are evaluated using entropy, while COPRAS-ARAS hybrid model is implemented to choose the best alternative experiment among these nine availabilities. Experiment 5 is found to be the best choice, with the machining parameters 261 µs pulse length, 0.3 kg/cm2 flushing pressure, 4.5 Å current, and 80 mm dielectric level being the optimal input values. The primary result demonstrates that the developed hybrid model is sufficiently accurate and provides the best choice when compared to six existing MCDM tools. Finally, sensitivity analysis is used to justify the hybrid model’s stability and consistency.

Разработка гибридной модели прогнозирования потребления электрической энергии для горно-металлургического предприятия

Introduction. Reducing the energy intensity of production is one of the directions of development of enterprises, regions, states, as it affects the level of economic development and environmental well-being. One of the methods that contribute to the rationalization of the use of electric energy is forecasting. The purpose of the study is to develop a model for forecasting electricity consumption, which allows obtaining a reliable forecast of electricity consumption. To achieve this goal, an analysis of literary sources reflecting the current trends of predictive analytics in solving the problem of forecasting electricity consumption was carried out. Materials and methods of research. Singular spectral analysis and deep learning algorithms (multilayer perceptron) were used in the work. At the initial stage of the study, a model of a fully connected neural network (multilayer perceptron) was built, the prediction accuracy of which was more than 94%. Then the algorithm of singular spectral analysis was implemented, and the initial time series of power consumption was decomposed into additive components. The resulting decompositions were used to create a hybrid forecasting model. Research results. A number of experimental studies have been conducted that meet the goal of developing models that most accurately approximate experimental data. The result of the study are two forecasting models: an artificial neural network model and a hybrid model that includes a singular spectral analysis to decompose a number of power consumption into trend and harmonic components and an artificial neural network to predict them. Discussion. With the help of the hybrid model, an accurate result was obtained – the average absolute percentage error for the year is 4.04%. Forecasting was carried out for a year (by months) using data from the previous two years as a training subset. The neural network model provides less accuracy in predicting power consumption over 12 months compared to the hybrid model. Conclusion. Thus, the use of singular spectral analysis made it possible to reduce the magnitude of the forecast error by almost 1%, which confirms the effectiveness of the method and the relevance of the development of hybrid forecasting models. Resume. As a result of the conducted research, it was revealed that one of the most popular and effective forecasting methods is singular spectral analysis and the use of various deep learning algorithms. The developed hybrid model of electric energy consumption for a mining and metallurgical enterprise allows you to build a forecast of electricity consumption, the accuracy of which is more than 95%. Suggestions for practical application and directions for future research. The research results can be useful in the process of production management, since an accurate forecast of electricity consumption is important when planning production, scheduling equipment repairs and maintenance of the electrical network. The prospect of further research is to increase the forecasting horizon, the volume of the initial sample and taking into account additional factors characterizing the technological process.

A hybrid XGBoost-SMOTE model for optimization of operational air quality numerical model forecasts

As a main technical tool, the air quality numerical model is widely used in the forecasts of atmospheric pollutants, and its development is of great significance to the atmospheric environment and human health. In this study, a hybrid XGBoost-SMOTE model has been developed and applied for the optimization of forecasted PM2.5 and O3 concentrations from the Chinese operational air quality forecasting model - CMA Unified Atmospheric Chemistry Environment model (CUACE), which automatically finds the optimal hyperparameters and features without human intervention. Supported by a knowledge base including the ground-observed, CUACE-forecasted pollutants and meteorological data as well as some auxiliary variables, and based on the evaluation analysis of 46 selected key national cities, it was found that the XGBoost-SMOTE model can achieve satisfactory optimization effects for the operational model, especially the significant improvement of the pollutant extreme values on high-pollution days. The results show that after optimization, the 5-day average correlation coefficient (R), mean error (ME) and root mean square error (RMSE) values can reach 0.87, 10.34 µg/m3 and 16.53 µg/m3 for PM25, and 0.89, 14.53 µg/m3 and 18.83 µg/m3 for O3, far better than those from original CUACE model and XGBoost model. Furthermore, the optimization of the spatial distribution of pollutants from the CUACE model and the impact analysis of the input features by the SHAP method were also explored. The developed hybrid model unveils a good application prospect in the field of environmental meteorology forecasts.

A training strategy for hybrid models to break the curse of dimensionality.

Mechanistic/data-driven hybrid modeling is a key approach when the mechanistic details of the processes at hand are not sufficiently well understood, but also inferring a model purely from data is too complex. By the integration of first principles into a data-driven approach, hybrid modeling promises a feasible data demand alongside extrapolation. In this work, we introduce a learning strategy for tree-structured hybrid models to perform a binary classification task. Given a set of binary labeled data, the challenge is to use them to develop a model that accurately assesses labels of new unlabeled data. Our strategy employs graph-theoretic methods to analyze the data and deduce a function that maps input features to output labels. Our focus here is on data sets represented by binary features in which the label assessment of unlabeled data points is always extrapolation. Our strategy shows the existence of small sets of data points within given binary data for which knowing the labels allows for extrapolation to the entire valid input space. An implementation of our strategy yields a notable reduction of training-data demand in a binary classification task compared with different supervised machine learning algorithms. As an application, we have fitted a tree-structured hybrid model to the vital status of a cohort of COVID-19 patients requiring intensive-care unit treatment and mechanical ventilation. Our learning strategy yields the existence of patient cohorts for whom knowing the vital status enables extrapolation to the entire valid input space of the developed hybrid model.

Overall Cost Overrun Estimate in Residential Projects: A Hybrid Dynamics Approach

Residential projects are described as complex, dynamic systems that are subject to uncertainty. Cost performance is a fundamental challenge. As a result, project managers must adequately identify risks that might lead to cost overruns in residential construction projects. Simulation is noticed to be a useful technique for dealing with these complications. Therefore, this study developed a hybrid dynamic approach to study the effect of different risks on the cost performance of construction projects. The proposed approach combines system dynamics (SD) and discrete event simulation (DES), which can take into consideration the dynamics of the project environment, which contains various continuous influencing factors as well as the construction operations. The developed hybrid model is validated through serial model structure tests and model behavior tests, with the aid of data collected from a real construction project used in the simulation process. Based on the simulation results, it is concluded that the proposed hybrid dynamic approach is helpful to enhance the process performance by permitting construction managers to identify possible process improvement areas that traditional methods may miss.

A hybrid approach based on complete ensemble empirical mode decomposition with adaptive noise for multi-step-ahead solar radiation forecasting

Abstract Accurate measurements of solar radiation are required to ensure that power and energy systems continue to function effectively and securely. On the other hand, estimating it is extremely challenging due to the non-stationary behaviour and randomness of its components. In this research, a novel hybrid forecasting model, namely complete ensemble empirical mode decomposition with adaptive noise–Gaussian process regression (CEEMDAN–GPR), has been developed for daily global solar radiation prediction. The non-stationary global solar radiation series is transformed by CEEMDAN into regular subsets. After that, the GPR model uses these subsets as inputs to perform its prediction. According to the results of this research, the performance of the developed hybrid model is superior to two widely used hybrid models for solar radiation forecasting, namely wavelet–GPR and wavelet packet–GPR, in terms of mean square error, root mean square error, coefficient of determination and relative root mean square error values, which reached 3.23 MJ/m2/day, 1.80 MJ/m2/day, 95.56%, and 8.80%, respectively (for one-step forward forecasting). The proposed hybrid model can be used to ensure the safe and reliable operation of the electricity system.

Insulator Leakage Current Prediction Using Hybrid of Particle Swarm Optimization and Gene Algorithm-Based Neural Network and Surface Spark Discharge Data.

This study proposes a new superior hybrid algorithm, which is the particle swarm optimization (PSO) and gene algorithm (GA)-based neural network to predict the leakage current of insulators. The developed algorithm was utilized for the online monitoring systems, which were completely installed on the 69 kV and 161 kV transmission towers in Taiwan. This hybrid algorithm utilizes the local meteorological data as input parameters combined with the extracted enhanced data: the percentage of spark discharge areas and the brightness change in the image of the discharge phenomenon. These data with a high correlation with the leakage current are utilized as input vectors to improve the accuracy and effectiveness of the developed hybrid model. The performance of the developed algorithm is compared with a traditional PSO-based neural network and backpropagation neural network (BPNN) to evaluate and analyze. The comparative simulation results prove the effectiveness of the combination of hybrid PSO-GA-based neural network and surface discharge data, which achieved a maximum improvement of 38.54% MSE, 10.62% MAPE, and 3.41% R square for 161 kV data and 39.28% MSE, 12.62% MAPE, and 1.61% R square for 69 kV data. Moreover, the data with enhanced inputs outperform the traditional data in most benchmark factors, improving the accuracy and effectiveness in defining the deteriorative insulators. The developed methodology with a noticeable improvement was utilized in the online monitoring system to reduce the operational and maintenance cost of transmission lines in Taiwan Power Company.

Investigating fault zone hydraulic properties and groundwater potential in a fault-dominated aquifer system: A case study of the Dili fault in Central Taiwan

Discovering groundwater resources in a fault-controlled aquifer system appears to be a viable solution to water scarcity in the mountainous areas of Taiwan. Regoliths, fractured bedrock, and faults commonly exist in such aquifer systems. To investigate the groundwater behavior in the three subsurface media, a 3-D hybrid model (equivalent porous media/discrete fracture network) involving various in situ investigation techniques (borehole drilling, outcrop investigation, borehole televiewer logging, sonic logging, and downhole hydraulic tests) was proposed to assess the hydraulic properties and groundwater storage of the Dili fault zone of Central Taiwan and its surrounding formations. Subsequently, the model was verified through a case study of a simple fracture network. Based on the validated model, flow path analysis was successfully performed to reveal the hydrogeological role of the fault zone, which acts as a conduit for groundwater flow. To ensure the simulated result regarding the hydraulic properties of the fault zone, this study proposes three additional cross-checking techniques, namely stable water isotopes, derivative plots of pumping test data, and identification of hydraulic gradients. The results of the investigation show that the hydraulic properties of the fault zone obtained by the three approaches are consistent with those determined by the numerical method, and the use of the three cross-checking techniques has been confirmed to be suitable for effectively exploring the hydraulic properties of a fault because the site characterization data are insufficient. Finally, the developed hybrid model was used to calculate the groundwater storage of each geological zone, indicating its great potential for the development of groundwater resources in the study area. Therefore, this study integrates in situ investigation techniques, numerical models, and three evaluation approaches of fault zone hydrogeology to construct a systematic, feasible, and applicable exploration methodology that can be employed for groundwater resource exploration in a fault-dominated aquifer system.

A new PM2.5 concentration forecasting system based on AdaBoost‐ensemble system with deep learning approach

AbstractA reliable and efficient forecasting system can be used to warn the general public against the increasing PM2.5 concentration. This paper proposes a novel AdaBoost‐ensemble technique based on a hybrid data preprocessing‐analysis strategy, with the following contributions: (i) a new decomposition strategy is proposed based on the hybrid data preprocessing‐analysis strategy, which combines the merits of two popular decomposition algorithms and has been proven to be a promising decomposition strategy; (ii) the long short‐term memory (LSTM), as a powerful deep learning forecasting algorithm, is applied to individually forecast the decomposed components, which can effectively capture the long‐short patterns of complex time series; and (iii) a novel AdaBoost‐LSTM ensemble technique is then developed to integrate the individual forecasting results into the final forecasting results, which provides significant improvement to the forecasting performance. To evaluate the proposed model, a comprehensive and scientific assessment system with several evaluation criteria, comparison models, and experiments is designed. The experimental results indicate that our developed hybrid model considerably surpasses the compared models in terms of forecasting precision and statistical testing and that its excellent forecasting performance can guide in developing effective control measures to decrease environmental contamination and prevent the health issues caused by a high PM2.5 concentration.

Critical thermal shock temperature prediction of alumina using improved hybrid models based on artificial neural networks and Shannon entropy

This study investigates the potential of a simple and Hybrid artificial neural network (ANN) to predict dense alumina's critical thermal shock temperature (ΔTc). The predictive models have been constructed using two ANNS models (M1, M2). In the first model (M1), elaboration, physical and mechanical parameters have been exploited to build three ANNs, namely generalized linear regression (M1-GLRNN), extreme learning machine (M1-ELM), and radial basis function (M1-RBFNN). The second model (M2) has been built by the three models mentioned above incorporated by the Shannon Entropy (SE) method. To compare the performance of all the developed models, coefficient of correlation (R), root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency coefficient (NSE) have been considered. It is found that M2-RBFNN model with (RMSE = 4.3526, MAPE= 0.3406, NSE = 0.9921, and R= 0.9960) had superiority to the M1-RBFNN model (RMSE = 4.7030, MAPE= 0.3003, NSE = 0.9908, and R = 0.9954). More importantly, the contribution of the present work is that prediction of ΔTc has been performed through the developed hybrid model (M2-RBFNN), which reduces the number of inputs from six to only four inputs and offers high accuracy for all the studied variables.

Research and Application of the Mode Decomposition-Recombination Technique Based on Sample-Fuzzy Entropy and K-Means for Air Pollution Forecasting

Practical forecasting of air pollution components is important for monitoring and providing early warning. The accurate prediction of pollutant concentrations remains a challenging issue owing to the inherent complexity and volatility of pollutant series. In this study, a novel hybrid forecasting method for hourly pollutant concentration prediction that comprises a mode decomposition-recombination technique and a deep learning approach was designed. First, a Hampel filter was used to remove outliers from the original data. Subsequently, complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) is employed to divide the original pollution data into a finite set of intrinsic mode function (IMF) components. Further, a feature extraction method based on sample-fuzzy entropy and K-means is proposed to reconstruct the main features of IMFs. In conclusion, a deterministic forecasting model based on long short-term memory (LSTM) was established for pollutant prediction. The empirical results of six-hourly pollutant concentrations from Baoding illustrate that the proposed decomposition-recombination technique can effectively handle nonlinear and highly volatile pollution data. The developed hybrid model is significantly better than other comparative models, which is promising for early air quality warning systems.

A Fusion-Based Technique With Hybrid Swarm Algorithm and Deep Learning for Biosignal Classification

The vital data about the electrical activities of the brain are carried by the electroencephalography (EEG) signals. The recordings of the electrical activity of brain neurons in a rhythmic and spontaneous manner from the scalp surface are measured by EEG. One of the most important aspects in the field of neuroscience and neural engineering is EEG signal analysis, as it aids significantly in dealing with the commercial applications as well. To uncover the highly useful information for neural classification activities, EEG studies incorporated with machine learning provide good results. In this study, a Fusion Hybrid Model (FHM) with Singular Value Decomposition (SVD) Based Estimation of Robust Parameters is proposed for efficient feature extraction of the biosignals and to understand the essential information it has for analyzing the brain functionality. The essential features in terms of parameter components are extracted using the developed hybrid model, and a specialized hybrid swarm technique called Hybrid Differential Particle Artificial Bee (HDPAB) algorithm is proposed for feature selection. To make the EEG more practical and to be used in a plethora of applications, the robust classification of these signals is necessary thereby relying less on the trained professionals. Therefore, the classification is done initially using the proposed Zero Inflated Poisson Mixture Regression Model (ZIPMRM) and then it is also classified with a deep learning methodology, and the results are compared with other standard machine learning techniques. This proposed flow of methodology is validated on a few standard Biosignal datasets, and finally, a good classification accuracy of 98.79% is obtained for epileptic dataset and 98.35% is obtained for schizophrenia dataset.

Hybrid modeling approach for the tether of an airborne wind energy system

The design of controllers for the automatic operation of airborne vehicles is challenging. Especially in the emerging research field of airborne wind energy, where tethered aircraft convert energy from wind at higher altitudes into electricity, the development of robust and safe control systems will be a critical success factor. Typically, these systems consist of one or more controlled aircraft, a ground station, and one or more tethers that transfer aerodynamic forces acting on the wings to the ground. To understand these systems and control them for real-time simulations, suitable tether models must extend the classical flight mechanical models. In this paper, a hybrid approach is presented, combining an elastic spring model with a catenary model of the tether. In particular, for real-time simulations in a model-in-the-loop environment where a static modeling approach is sufficient, this hybrid model is intended to provide an alternative to the commonly used spring models. Furthermore, such a static tether model neglects high-frequency dynamic oscillations that can occur with dynamic lumped mass models. As consequence, the focus is on primary influences such as stiffness and sag due to external loads. The developed hybrid model is analyzed and compared with the spring model for a simulated tethered flight. It can be concluded that this hybrid static model, although subject to strong assumptions such as symmetrical tether sag, represents the tether more accurately. For example, it allows a more precise design of the flight controller before conducting any flight tests. This model can be used as a reasonable alternative to the simple spring model whenever a static modeling approach of the tether is adequate.

Opposition-Based Deer Hunting Optimization-Based Hybrid Classifier for Intrusion Detection in Wireless Sensor Networks

This paper tempts to implement a new machine-learning algorithm for detecting attacks in WSN. The developed model involves three main phases (a) Data Acquisition, (b) Feature Extraction, and (c) Detection. Next to the data acquisition from different benchmark datasets, the attributes in the form of features are extracted. Further, a new hybrid machine learning algorithm with the integration of Neural Network (NN), and Fuzzy Classifier is used for detection, and it is termed as FNN. As an improvement to the developed hybrid model, the number of hidden neurons in NN, and the membership function of Fuzzy Classifier is optimized by a modified optimization algorithm called Opposition-based Deer Hunting Optimization Algorithm (O-DHOA). Finally, the experiment analysis of our proposed model provides an effective solution to solve the problem of IDS detection and improves the performance of intrusion detection.