This paper deals with the application of the designed algorithm in automatic generation control (AGC) problem of different structures, such as interconnected and isolated. The important contribution made by this paper...
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(纸本)9798350332117
This paper deals with the application of the designed algorithm in automatic generation control (AGC) problem of different structures, such as interconnected and isolated. The important contribution made by this paper is the application of the validated algorithm in the conventional four-area interconnected model and isolated power system model with different operating conditions to study AGC performance. In the studied systems, a proportional-integral-derivative PID controller is employed, and its parameters are tuned by the chimp optimization algorithm (CHOA), chaotic chimp optimization algorithm (C-CHOA) and chaotic chimp sine cosine optimization (C-CHOA-SC) algorithm methods. Finally, the results are validated by real-time simulation results using d-SPACE.
Image caption generation is becoming one of the hot research topics and attracts various researchers. It is a complex process because it utilizes both NLP (natural language processing) and computer vision approaches f...
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Image caption generation is becoming one of the hot research topics and attracts various researchers. It is a complex process because it utilizes both NLP (natural language processing) and computer vision approaches for generating the tasks. A range of strategies are available for image captioning that connect the visual material with everyday language, such as explaining images with textual descriptions. Pre-trained classification networks like CNN and RNN-based neural network models are used in the literature to encrypt visual data. Even though various literature works have analyzed outstanding image caption techniques, they still lack in providing better performance for diverse databases. To overcome such issues, this research work presents an automated optimization deep learning model for image caption generation. Initially, the input image is pre-processed, and then the encoder decoder-based structure is utilized for extracting the visual features and caption generation. On the encoder side, the pre-trained ResNet 101 (residual network) is used to extract the visual features, and the SA- Bi-LSTM (self-attention with bi-directional Long Short-Term Memory) is used to generate the caption on the decoder side. In addition, an optimization model CA (chimp algorithm) is used to improve detection performance in caption generation. The proposed encoder-decoder model is tested on benchmark datasets like Flickr8k, Flickr30k and COCO. Further, this model attained better BLEU and ribes scores of 0.8595 and 0.3531 on the Flickr8k dataset. Thus, the proposed SA-BiLSTM model achieved a significant performance in image caption generation.
Digital data security has grown rapidly based on the advances of smart applications. Hence, the data is secured in several ways, like malicious prediction, avoidance, etc. However, classifying and preventing malicious...
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Digital data security has grown rapidly based on the advances of smart applications. Hence, the data is secured in several ways, like malicious prediction, avoidance, etc. However, classifying and preventing malicious actions is difficult because some malicious actions behave like normal users. When the data is entered, it captures it and does malicious activities. So, the current article was planned to build a novel chimp (You-Only-Look-Once) YOLO Malicious Avoidance Framework (CbYMAF) as the attack recognition and prevention mechanism. Here, the data was initialized in the primary stage, and then the noise constraints were neglected through the pre-processing function. Henceforth, the features are extracted, and the malicious actions are recognized. Finally, the malicious types were categorized, and the prevention module's features were updated to prevent malicious events. Besides, the unknown attack was launched to value the designed approach's confidentiality ratio. Finally, the Python framework validates the novel CbYMAF, and the comparative analysis is conducted with past works.
Recycled powder (RP) has emerged as a promising and viable alternative to traditional cementitious materials for use in concrete. The compressive strength (f(C)) of RP mortar has a considerable impact on the mechanica...
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Recycled powder (RP) has emerged as a promising and viable alternative to traditional cementitious materials for use in concrete. The compressive strength (f(C)) of RP mortar has a considerable impact on the mechanical properties of RP concrete. Utilizing machine learning approaches to engineering problems, particularly when estimating the mechanical properties of construction materials, results in outstanding accuracy in forecasting and minimal experimental costs. This study aimed to provide some integrated machine-learning techniques for estimating the f(C) of recycled powder mortar (RPM). Initially, relevant literature is consulted to acquire data on the f(C) test results of 204 groups of mortars. Subsequently, the Extreme Gradient Boosting (XGB), Random Forest (RF), and Support Vector Regression (SVR) methodologies are used, followed by the optimization of their respective hyperparameters using the chimp optimization algorithm (ChOA), in order to construct powerful forecasting approaches (XGB(ChO)A, RFChOA and SVRChOA). According to the results, all three models exhibit excellent results in correctly anticipating the fC. By leveraging these advanced machines learning techniques and optimizing them with ChOA, the authors intended to achieve high accuracy in their predictions, thereby reducing the need for extensive experimental testing and minimizing costs associated with traditional methods of estimating the mechanical properties of construction materials. By accurately predicting the fC of RPM, these models can significantly reduce the need for extensive physical testing, leading to cost savings in material research and development. While the study mentions the generalization ability of the models, it would be beneficial to assess their performance on independent datasets or in real-world applications to confirm their practical utility. Including external factors or environmental conditions factors in the analysis could enhance the model's accuracy and robustne
In May 2022, monkeypox re-emerged as a rare zoonotic disease that is an important viral disease for public health. Monkeypox can be transmitted from animals to humans, between humans through close contact with an infe...
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In May 2022, monkeypox re-emerged as a rare zoonotic disease that is an important viral disease for public health. Monkeypox can be transmitted from animals to humans, between humans through close contact with an infected human, or with a virus stained substance. Through this paper, a new detection strategy based on artificial intelligence techniques is provided to early detect monkeypox patients. This strategy is called Human Monkeypox Detection (HMD) strategy and mainly consists of two main phases, which are;(i) Selection Phase (SP) and (ii) Detection Phase (DP). While SP tries to select the best features, DP tries to introduce fast and accurate detection based on valid data from SP. In SP, an Improved Binary chimp Optimization (IBCO) algorithm as a new feature selection algorithm is introduced to select valuable features before learning an Ensemble Diagnosis (ED) model as a new diagnostic algorithm in the next phase called DP. In fact, the proposed IBCO algorithm is a hybrid selection algorithm that includes both filter and wrapper methods. IBCO consists of a filter layer called Filter Selection Layer (FSL) and a wrapper layer called Wrapper Selection Layer (WSL). At first, monkeypox dataset is entered into FSL to quickly select meaningful features by using 'm' filter selection techniques. Then, 'm' sets of selected features are fed into WSL to construct the initial population of Binary chimp Optimization (BCO) algorithm to precisely choose the best set of features for the next phase (DP). Finally, the ED model will be correctly trained on the filtered data from FSL. This model consists of three diagnostic algorithms called Weighted Naive Bayes (WNB), Weighted K-Nearest Neighbors (WKNN), and deep learning which are combined using a new weighted voting method to provide the best diagnostic results. The weighted values of WNB algorithm are determined by measuring the impact of each feature on the class categories while the Grey Wolf Optimization (GWO) algorithm is
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