The objective of this paper is to contribute to the sharing, transfer, and secured indexing of large color images. Watermarking and digital encryption have emerged as alternative and complementary solutions to ensure ...
详细信息
The objective of this paper is to contribute to the sharing, transfer, and secured indexing of large color images. Watermarking and digital encryption have emerged as alternative and complementary solutions to ensure authorized access, facilitate authentication of content, or prevent illegal reproduction. For this reason, we suggest a new system based on four techniques derived from various transformation and decomposition methods, such as the discrete orthogonal Racah moment transformations (DORMT), which allows the information capacity to be reduced to a number of coefficients, as well as the discrete wavelet transform (DWT), which makes it possible to decompose into a certain number of multilevel sub-bands, to have the sub-band that represents most of the image information. In addition, Schur decomposition (SD) and singular value decomposition (SVD) have been used to increase security. The proposed system consists of two main phases. The first phase is the integration of a color watermark, first applying DORMT-SD-SVD to a color host image and then applying DWT-SD-SVD to achieve a compromise between capacity, invisibility and robustness in terms of quality and privacy. The second phase is the encryption of the watermarked image with an e pi map to increase security for sharing, transfer and indexing. This system uses an aquila optimization algorithm (AOA) as an optimization procedure to find the best set of DORMT parameters and provide a dynamic and adaptive scale factor. The experimental results, in which the proposed system's properties are tested and compared to those of other systems, make it clear that the proposed system is useful in terms of its level of security, capacity, invisibility, and resistance to attacks that use signal processing.
Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in wat...
详细信息
Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the aquilaoptimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the aquilaalgorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75 s, a precision of 93.9%, a recall rate of 95.1%, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.
To accommodate China's electricity market reforms integrating medium and long-term (MLT) transactions and spot transactions, and to boost renewable energy consumption through the spot market, this paper proposes a...
详细信息
To accommodate China's electricity market reforms integrating medium and long-term (MLT) transactions and spot transactions, and to boost renewable energy consumption through the spot market, this paper proposes an optimized cross-provincial electricity trading strategy model based on a two-layer game framework. The proposed model incorporates an MLT green certificate contract decomposition method, enabling nested optimization of green certificate contracts and scheduling plans for cross-provincial power transactions. To encourage broader participation, a bilateral green certificate trading framework is established, which globally optimizes green certificate allocation to increase benefits for market participants. A Nash-Stackelberg game model is introduced to address complex game interactions among multiple participants under the green certificate mechanism and the limitation of assuming complete rationality. The game model combines supply and demand sides with an embedded demand-side evolutionary game. Additionally, an improved aquila optimization algorithm (IAOA) is developed to accurately calculate electricity supply and demand. The algorithm integrates a Circle chaotic map, Sobol sequence, random walk strategy, and filtering technology to enhance optimization capabilities and manage complex constraints. The algorithm is then embedded with a distributed iterative approach to achieve equilibrium strategies. A real-world case study was conducted to validate the feasibility and effectiveness of the proposed model. The results demonstrate that the proposed approach effectively achieves equilibrium, optimizes trading strategies, and fosters win-win, coordinated development among participants in the cross-provincial electricity market.
Electric vehicles (EVs) as a sustainable safety system are being increasingly used and receiving attention from researchers for several reasons including optimal performance, affordability for consumers, and environme...
详细信息
Electric vehicles (EVs) as a sustainable safety system are being increasingly used and receiving attention from researchers for several reasons including optimal performance, affordability for consumers, and environmental safety. EV speed control is a crucial issue that requires reliable and intelligent controllers for maintaining this matter. The primary goal of this research is to design the linear and nonlinear Proportional, Integral, and Derivative (PID) controllers to control EV speed based on the minimum value of ITAE plus ISU (integral square of control signal) as well as satisfy the constrain on response overshoot. All the proposed PID controllers, conventional PID controller, arc tan PID controller, and nonlinear PID controller (NL-PID) are used in cascade with EV model. In all these PID controllers, a filter is used with the derivative term to avoid the effect of the noise. The tuning of the proposed controller gains is achieved using aquila optimization algorithm. The controllers' parameter tuning is primarily determined by reducing the Integral Time Absolute Error (ITAE) and integral square control signal. Numerical simulation, system modelling, and controller design are done using MATLAB. By comparing the results, the proposed controllers' efficacy is demonstrated. The proposed NL-PID controller provides promising EV speed regulation control and robustness to external disturbances. Where, the performance specifications of the proposed NL-PID controller when using unit step input with step disturbance of 0.2 and -0.2, and the model parameters increased by 25% from its nominal value are the rise time is 2.608, settling time is 2.608, overshoot is 0.073%, the maximum control signal is 6.230, number of slope sign change in the control signal is 231, and ITAE is 17.6064. The comparative results show the NL-PID controller's superior performance for tracking the reference signal with the lowest peak for the control signal, rejection of disturbances, ability to
Wind speed prediction has received reasonable attention recently because of its clean and promising source of renewable energy. Recent studies have shown that developing efficient model to predict wind speed is a chal...
详细信息
Wind speed prediction has received reasonable attention recently because of its clean and promising source of renewable energy. Recent studies have shown that developing efficient model to predict wind speed is a chal-lenging task because of its nonlinear and stochastic characteristics. This paper aims to propose a new hybrid model to predict wind speed. For this purpose, Discrete Wavelet Transform (DWT), Phase Space Reconstruction (PSR) of chaos theory, aquila optimization algorithm (AOA) and Backpropagation Neural Network (BPNN) are hybridised and a novel DWT-PSR-AOA-BPNN is proposed. To ascertain the proposed DWT-PSR-AOA-BPNN model performance, different hybrid model variants (DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN) were developed for comparison. The comparison was done using statistical model evaluators of Mean Average Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and model efficiency of Loague and Green (E-LG). The statistical results showed that the proposed DWT-PSR-AOA-BPNN model performed better and is therefore considered efficient wind speed prediction tool when compared with DWT-PSR-GA-BPNN, DWT-PSR-PSO-BPNN, PSR-PSO-BPNN and PSR-AOA-BPNN hybrid models. That is, the proposed DWT-PSR-AOA-BPNN had the lowest MAE, RMSE and MAPE values for the model testing (MAE = 1.1490, RMSE = 1.4190 and MAPE = 0.2743) and validation (MAE = 0.8122, RMSE = 0.9771 and MAPE = 0.1953). The DWT-PSR-AOA-BPNN also achieved the highest ELG values of 0.9904 (testing) and 0.99738 (validation) respectively. It is therefore concluded that considering the DWT-PSR-AOA-BPNN results, the indication corroborates the fact that this model can be utilized for efficient grid operations.
暂无评论