The volatility of contact resistance directly reflects the fluctuation of friction pairs in the pantograph-catenary system, so this paper proposes a new prediction model of contact resistance volatility based on an im...
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The volatility of contact resistance directly reflects the fluctuation of friction pairs in the pantograph-catenary system, so this paper proposes a new prediction model of contact resistance volatility based on an improved pelican optimization algorithm and optimized extreme learning machine to judge the magnitude of contact resistance volatility. The paper first examines the volatility of contact resistance of pantograph-catenary under fluctuating loads. It concludes that it has an inverse relationship with sliding velocity and pressure fluctuation frequency but a positive correlation with the given current and pressure fluctuation amplitude. Secondly, the pelican optimization algorithm (POA) is enhanced to improve its stability, accuracy, and speed. The simulation results demonstrate that the improved pelican optimization algorithm (IPOA) outperforms the standard POA. Finally, the IPOA-ELM model is developed by optimizing the extreme learning machine (ELM) parameters with IPOA. The experimental results show that the IPOA-ELM model is highly effective in predicting contact resistance volatility.
The connection of renewable energy sources such as wind and solar power into the power grid can significantly reduce both costs and pollution emissions. However, the variability, volatility, and anti -peak regulation ...
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The connection of renewable energy sources such as wind and solar power into the power grid can significantly reduce both costs and pollution emissions. However, the variability, volatility, and anti -peak regulation characteristics of renewable energy pose significant challenges for power system dispatch. This paper proposes a hybrid economic emission dispatch model (HDEED) for wind-solar-thermal-storage systems, with operational cost and pollution emission as objective functions. The study aims to develop optimal grid -connection strategies for clean energy by utilizing the energy -shifting capability of energy storage systems. This includes strategies based on optimal load fluctuation and optimal operation income for new energy stations. A generalized load fluctuation coefficient is proposed to assess load fluctuations after wind and solar energy integration, comparing and analyzing the performance of energy storage power stations with varying capacities. In terms of algorithm development, the paper proposes the pelican optimization algorithm with a clustering strategy (POA-CS), specifically tailored to address the complexities of economic emission scheduling. The effectiveness of the proposed strategy and algorithm is validated using an enhanced IEEE -39 bus test system. Results indicate that the generalized load fluctuation coefficient under the optimal grid -connected strategy based on load fluctuation is 21% lower than that of direct grid connection of wind power and photovoltaic, leading to a significant reduction in net load fluctuation. Furthermore, under the optimal grid -connected strategy based on the operation income of new energy stations, the revenue of these plants increased by 22.40% compared to direct grid connections of wind power and photovoltaic systems. The POA-CS algorithm demonstrates superior performance, continuity, and smoothness in obtaining the Pareto optimal boundary under consistent testing conditions.
Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human *** sudden and violent failures of rock masses are characterized by the rapid release o...
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Rock bursts represent a formidable challenge in underground engineering,posing substantial risks to both infrastructure and human *** sudden and violent failures of rock masses are characterized by the rapid release of accumulated stress within the rock,leading to severe seismic events and structural ***,the development of reliable prediction models for rock bursts is paramount to mitigating these *** study aims to propose a tree-based model—a Light Gradient Boosting Machine(LightGBM)—to predict the intensity of rock bursts in underground engineering.322 actual rock burst cases are collected to constitute an exhaustive rock burst dataset,which serves to train the *** population-basedmetaheuristic algorithms are used to optimize the hyperparameters of the LightGBM ***,the sensitivity analysis is used to identify the predominant factors that may incur the occurrence of rock *** results show that the population-based metaheuristic algorithms have a good ability to search out the optimal hyperparameters of the LightGBM *** developed LightGBM model yields promising performance in predicting the intensity of rock bursts,with which accuracy on training and testing sets are 0.972 and 0.944,*** sensitivity analysis discloses that the risk of occurring rock burst is significantly sensitive to three factors:uniaxial compressive strength(σc),stress concentration factor(SCF),and elastic strain energy index(Wet).Moreover,this study clarifies the particular impact of these three factors on the intensity of rock bursts through the partial dependence plot.
Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challen...
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Challenges in the operation of power systems arise from several factors such as the interconnection of large power systems, integration of new energy sources and the increase in electrical energy demand. These challenges have required the development of fast and reliable tools for evaluating the operation of power systems. The load margin (LM) is an important index in evaluating the stability of power systems, but traditional methods for determining the LM consist of solving a set of differential-algebraic equations whose information may not always be available. Data-Driven techniques such as Artificial Neural Networks were developed to calculate and monitor LM, but may present unsatisfactory performance due to difficulty in generalization. Therefore, this article proposes a design method for Physics-Informed Neural Networks whose parameters will be tuned by bio-inspired algorithms in an optimization model. Physical knowledge regarding the operation of power systems is incorporated into the PINN training process. Case studies were carried out and discussed in the IEEE 68-bus system considering the N-1 criterion for disconnection of transmission lines. The PINN load margin results obtained by the proposed method showed lower error values for the Root Mean Square Error (RMSE), Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE) indices than the traditional training Levenberg-Marquard method.
Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of ...
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Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based pelican Whale optimizationalgorithm (AT+SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance. The Arnold transform used in this work provides security by encrypting the data. The use of SqueezeNet makes the proposed model a simple design and this reduces the computational time. Thus, the AT+SqeezeNet_PWOA attained better correlation coefficient (CC), peak signal-to-noise ratio (PSNR) and mean square error (MSE) of 0.908, 48.66 and 0.001 dB with the Gaussian noise.
In the field of cognitive networks, particularly within the Internet of Things and wireless sensor networks, secure and energy-efficient data transmission is crucial. Traditional methods often fall short in optimizing...
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In the field of cognitive networks, particularly within the Internet of Things and wireless sensor networks, secure and energy-efficient data transmission is crucial. Traditional methods often fall short in optimizing energy and enhancing security during data transmission. Effective key management schemes that are both secure and energy-efficient are still lacking. Moreover, many challenges remain based on high computational overhead, diversity, low throughout, and latency issues, which lead to poor performance. To find the solution for aforementioned issues, this research proposes a novel Multipath Link Routing Protocol for secure and energy-aware data transmission, leveraging optimal cluster head selection within cognitive Internet of Things-wireless sensor networks applications. The sensor data is collected from the environment using sensor nodes. The novel Hybrid pelican optimization algorithm based Sine Cosine algorithm is suggested to choose optimal clusters during cluster generation. In this, the Sine Cosine algorithm is integrated into the pelican optimization algorithm to mitigate diversity challenges and ensure proper balancing. Then, the Multipath Link Routing Protocol approach implements numerous paths for dependable and effective data transfer across sensor nodes. Here, pair wise keys are shared by Multipath Link Routing Protocol approach between adjacent nodes to guarantee secure transmission. Additionally, the implementation of homomorphic encryption technique encrypts and decrypts messages to address the key distribution issue for securing transmission during secret key generation. Furthermore, data degradation and retransmission are avoided by this protocol, which uses query, routing request, and route reply to identify the routing from source node to destination node. After that, the creation of a blockchain-assisted authentication framework ensures safe access while guarding against illegal access in cloud-stored data. By encrypting the input data
As per the World Health Organization (WHO), the justifications of people with disabilities globally are restricted by physical and social barriers that exclude their full contribution to society. Constructed environme...
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As per the World Health Organization (WHO), the justifications of people with disabilities globally are restricted by physical and social barriers that exclude their full contribution to society. Constructed environment barriers can limit the availability of transportation, employment, goods and services, healthcare, and overall independent drive. The government of Saudi Arabia has applied programs and policies to enhance the quality of life for people with disabilities, including education, healthcare, and employment chances. Furthermore, they also take action to progress a few social guards that endorse public involvement and income-support plans for individuals with disabilities, besides efforts to uphold the cultural, social, political, and economic environment for accurate plans. Therefore, this study presents the Effectiveness of Machine Learning Models for estimating the Financial Cost of Assistive Services to Disability Care (EMLM-EFCASDC) technique in the KSA. The presented EMLM-EFCASDC technique mainly aims to develop a data-driven model that accurately predicts the cost of assistive services in disability care across the KSA. At first, the EMLM-EFCASDC approach utilizes Z-score normalization to preprocess the input data, ensuring that data variability is minimized for improved model accuracy. Next, an ensemble of machine learning (ML) models comprises three classifiers such as hybrid kernel extreme learning machine (HKELM), extreme gradient boosting (XGBoost), and support vector regression (SVR) for predicting the financial cost. Eventually, the modified pelican optimization algorithm (MPOA) is utilized to fine-tune the optimal hyperparameter of ensemble model parameters to achieve high predictive performance. An extensive range of simulation analyses are employed to ensure the enhanced performance of the EMLM-EFCASDC technique. The performance validation of the EMLM-EFCASDC method portrayed the least RMSLE value of 0.1154 on existing approaches in terms
Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, t...
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Analyzing biomedical images is vital in permitting the highest-performing imaging and numerous medical applications. Determining the analysis of the disease is an essential stage in handling the patients. Similarly, the statistical value of blood tests, the personal data of patients, and an expert estimation are necessary to diagnose a disease. With the growth of technology, patient-related information is attained rapidly and in big sizes. Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. This study presents a novel Computer-Aided Diagnosis of Haematologic Disorders Detection Based on Spatial Feature Learning Networks with Hybrid Model (CADHDD-SFLNHM) approach using Blood Cell Images. The main aim of the CADHDD-SFLNHM approach is to enhance the detection and classification of haematologic disorders. At first, the Sobel filter (SF) technique is utilized for preprocessing to improve the quality of blood cell images. Additionally, the modified LeNet-5 model is used in the feature extractor process to capture the essential characteristics of blood cells relevant to disorder classification. The convolutional neural network and bi-directional gated recurrent unit with attention (CNN-BiGRU-A) method is employed to classify and detect haematologic disorders. Finally, the CADHDD-SFLNHM model implements the pelican optimization algorithm (POA) method to fine-tune the hyperparameters involved in the CNN-BiGRU-A method. The experimental result analysis of the CADHDD-SFLNHM model was accomplished using a benchmark database. The performance validation of the CADHDD-SFLNHM model portrayed a superior accuracy value of 97.91% over other
Tidal energy is a new type of clean energy, the development and utilization of it has great scientific research potential, practical application value and broad development prospect. Because the generation process of ...
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Tidal energy is a new type of clean energy, the development and utilization of it has great scientific research potential, practical application value and broad development prospect. Because the generation process of tidal energy involves many complex ocean dynamic factors, and the method of predicting tidal energy is relatively simple, it is a meaningful work to research its prediction. Aiming at the nonlinear and nonstationary characteristics of tidal energy, propose a tidal energy prediction method based on improved time-varying filter- empirical mode decomposition with pelican optimization algorithm (POA-TVF-EMD) and confluent double- stream neural network (CNN-LSTM). Firstly, propose a sum of refined composite multiscale dispersion entropy and Spearman correlation coefficient as the fitness function of the search process, and use POA-TVF-EMD to decompose tidal energy into a string of intrinsic mode functions. Secondly, use CNN-LSTM to predict each component, and reconstruct the component prediction result to get the initial prediction result. Then, calculate the difference between the original signal and the initial prediction result to get the error result, bring the error result back to CNN-LSTM for prediction to get the error prediction result, and take the average of the error result and the error prediction result to get the error correction result. Finally, reconstruct the initial prediction result and the error correction result to get the final prediction result. Take tidal energy data in Texas, Caribbean and Washington as case study, and design ten prediction methods for comparative experiment. Take tidal energy data of Washington as an example, coefficient of determination, root mean square error, mean absolute error and mean absolute percentage error of the prediction results of proposed prediction method are 0.99873, 0.10695, 0.08189 and 0.00965 respectively. The experimental result shows that proposed prediction method is superior to all other comp
Arrhythmia is the medical term for any irregularities in the normal functioning of the heart. Due to their ease of use and non-invasive nature, electrocardiograms (ECGs) are frequently used to identify heart problems....
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Arrhythmia is the medical term for any irregularities in the normal functioning of the heart. Due to their ease of use and non-invasive nature, electrocardiograms (ECGs) are frequently used to identify heart problems. Analyzing a huge number of ECG data manually by medical professionals uses excessive medical resources. Consequently, identifying ECG characteristics based on machine learning has become increasingly popular. However, these conventional methods have some limitations, including the need for manual feature recognition, complex models, and lengthy training periods. This research offers a unique hybrid POA-F3DCNN method for arrhythmia classification that combines the pelican Optimisation algorithm with fuzzy-based 3D-CNN (F3DCNN) to alleviate the shortcomings of the existing methods. The POA is applied to hyper-tune the parameters of 3DCNN and determine the ideal parameters of the Gaussian Membership Functions used for FLSs. The experimental results were obtained by testing the performance of five and thirteen categories of arrhythmia classification, respectively, on UCI-arrhythmia and the MIT-BIH Arrhythmia datasets. Standard measures such as F1-score, Precision, Accuracy, Specificity, and Recall enabled the classification results to be expressed appropriately. The outcomes of the novel framework achieved testing average accuracies after ten-fold crossvalidation are 98.96 % on the MIT-BIH dataset and 99.4% on the UCI arrhythmia datasets compared to state-of-the-art approaches.
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