Search Results (2,339)

Watermarking is a technique used to address issues related to the widespread use of the internet, such as copyright protection, tamper localization, and authentication. However, most watermarking approaches negatively affect the quality of the original image. In this research, we propose an optimized image watermarking approach that utilizes the dual-tree complex wavelet transform and particle swarm optimization algorithm. Our approach focuses on maintaining the highest possible quality of the watermarked image by minimizing any noticeable changes. During the embedding phase, we break down the original image using a technique called dual-tree complex wavelet transform (DTCWT) and then use particle swarm optimization (PSO) to choose specific coefficients. We embed the bits of a binary logo into the least significant bits of these selected coefficients, creating the watermarked image. To extract the watermark, we reverse the embedding process by first decomposing both versions of the input image using DTCWT and extracting the same coefficients to retrieve those corresponding bits (watermark). In our experiments, we used a common dataset from watermarking research to demonstrate the functionality against various watermarked copies and peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics. The PSNR is a measure of how well the watermarked image maintains its original quality, and the NCC reflects how accurately the watermark can be extracted. Our method gives mean PSNR and NCC of 80.50% and 92.51%, respectively. Full article

To investigate the controllability of sensitive cable forces during the construction phase of cable-stayed bridges, a novel optimization method is proposed, based on BP neural networks, which combines Gaussian process prediction with a simulated annealing-optimized particle swarm algorithm to determine the tolerance intervals of construction cable forces. Based on the analysis results of multiple linear regression, the variables for optimization are identified, and a mapping relationship between the sensitive cable forces and displacement values is established using a BP neural network. Subsequently, a Gaussian process model is constructed to delineate the relationship between cable forces and reliability, with a focus on the reliability of displacements during the construction phase of the cross-section, specifically targeting sensitive cable forces. Finally, a combination of the simulated annealing algorithm and the particle swarm algorithm is employed to optimize the tolerance intervals of the cable forces. To validate the effectiveness of the proposed optimization method, a case study is conducted on the tolerance interval optimization of cable forces using a three-tower steel box girder cable-stayed bridge. In this study, the construction cable forces are treated as optimization variables, while the reliability of displacements at both the main girder section and the tower’s top section serve as the optimization objectives and constraint conditions. Under the premise of ensuring structural reliability, the accurate tolerance range for the stay cable forces during the construction phase of the cable-stayed bridge is obtained. The results indicate that the traditional PSO algorithm stabilizes after 26 iterations, whereas the hybrid intelligent algorithm reaches stability after just 13 iterations. In addition, the hybrid algorithm shows a significant increase in the objective function value during early iterations, demonstrating stronger optimization capability. This indicates that the optimization method exhibits better convergence and superior global optimization capability. It effectively improves the compatibility and controllability of the cable-stayed bridge construction process while simplifying the computational process. Full article

Hybrid renewable-hydrogen energy systems offer a promising solution for meeting the globe’s energy transition and carbon neutrality goals. This paper presents a new multi-objective dynamic system model for the optimal sizing and simulation of hybrid PV-H₂ energy systems within grid-connected buildings. The model integrates a Particle Swarm Optimisation (PSO) algorithm that enables minimising both the levelised cost of energy (LCOE) and the building carbon footprint with a dynamic model that considers the real-world behaviour of the system components. Previous studies have often overlooked the electrochemical dynamics of electrolysers and fuel cells under transient conditions from intermittent renewables and varying loads, leading to the oversizing of components. The proposed model improves sizing accuracy, avoiding unnecessary costs and space. The multi-objective model is compared to a single-objective PSO-based model that minimises the LCOE solely to assess its effectiveness. Both models were applied to a case study within Robert Gordon University in Aberdeen, UK. Results showed that minimising only the LCOE leads to a system with a 1000 kW PV, 932 kW electrolyser, 22.7 kg H₂ storage tank, and 242 kW fuel cell, with an LCOE of 0.366 £/kWh and 40% grid dependency. The multi-objective model, which minimises both the LCOE and the building carbon footprint, results in a system with a 3187.8 kW PV, 1000 kW electrolyser, 106.1 kg H₂ storage tank, and 250 kW fuel cell, reducing grid dependency to 33.33% with an LCOE of 0.5188 £/kWh. Full article

This study presents a method for the active control of a follow-up lower extremity exoskeleton rehabilitation robot (LEERR) based on human motion intention recognition. Initially, to effectively support body weight and compensate for the vertical movement of the human center of mass, a vision-driven follow-and-track control strategy is proposed. Subsequently, an algorithm for recognizing human motion intentions based on machine learning is proposed for human-robot collaboration tasks. A muscle–machine interface is constructed using a bi-directional long short-term memory (BiLSTM) network, which decodes multichannel surface electromyography (sEMG) signals into flexion and extension angles of the hip and knee joints in the sagittal plane. The hyperparameters of the BiLSTM network are optimized using the quantum-behaved particle swarm optimization (QPSO) algorithm, resulting in a QPSO-BiLSTM hybrid model that enables continuous real-time estimation of human motion intentions. Further, to address the uncertain nonlinear dynamics of the wearer-exoskeleton robot system, a dual radial basis function neural network adaptive sliding mode Controller (DRBFNNASMC) is designed to generate control torques, thereby enabling the precise tracking of motion trajectories generated by the muscle–machine interface. Experimental results indicate that the follow-up-assisted frame can accurately track human motion trajectories. The QPSO-BiLSTM network outperforms traditional BiLSTM and PSO-BiLSTM networks in predicting continuous lower limb motion, while the DRBFNNASMC controller demonstrates superior gait tracking performance compared to the fuzzy compensated adaptive sliding mode control (FCASMC) algorithm and the traditional proportional–integral–derivative (PID) control algorithm. Full article

Regional power grid load has strong periodicity and randomness, and its load characteristics are affected by many factors. Traditional short-term power load-forecasting methods have certain limitations in accuracy and stability, especially when dealing with complex weather and voltage changes. To improve the prediction accuracy, this paper proposes a short-term power load-forecasting model of a regional power grid based on multi-source parameter coupling with a long short-term memory neural network (LSTM) and adopts an improved particle swarm optimization (IPSO) algorithm to optimize the LSTM network. Firstly, load characteristics under different time dimensions were analyzed, combined with meteorological factors such as daily maximum temperature, minimum temperature, rainfall, air humidity, and historical load data, multi-source data were coupled, and date types were quantified by independent thermal coding technology to ensure a reasonable model input data set. Different from traditional methods, this paper introduces real-time coupling data of intensive sensing, which makes the model more sensitive to capture the subtle characteristics of load changes. In order to further optimize the performance of the LSTM model, the IPSO algorithm, and linear difference decreasing inertia weight are introduced to improve the global optimization ability and convergence speed of the PSO algorithm and reduce the risk of local optimal solutions. Finally, the accuracy of the model is verified by the measured data of dense sensing in a regional power grid in northern China. The calculation results show that the prediction model based on multi-source parameter coupling has higher accuracy and stability in short-term load forecasting. Compared with traditional forecasting methods, the mean relative error (MAPE), the root mean square error (RMSE), and the mean absolute error (MAE) are reduced by 1.8149%, 154.0884, and 130.6769, respectively. In the typical day prediction of different seasons, the model can keep the relative error of more than 90% sampling points below 2%. The average relative error is basically less than 1%. The model proposed in this paper shows higher accuracy and stronger practical application potential compared with traditional forecasting methods, especially in voltage monitoring and power grid dispatching. Full article

Accurate prediction of the height of water-conducting fissure zone (HWCFZ) is an important issue in coal water control and a prerequisite for ensuring the safe production of coal mines. At present, the prediction model of HWCFZ has some issues such as poor prediction accuracy. Based on the widely collected measured data of the HWCFZ in different coal mines in northern Shaanxi Province, China, the HWCFZ in shallow-buried coal seams is categorized into two types, i.e., typical shallow-buried coal seams and near-shallow-buried seams, according to the different depths of burial and base-loading ratios. On the basis of summarizing the research results of the previous researchers, three factors, namely, mining thickness, coal seam depth, and working length, were selected, and the data of the height of the water-conducting fissure zone in the study area were analyzed by using a multivariate nonlinear regression method. Subsequently, each group of the data was randomly divided into training data and validation data with a ratio of 70:30. Then, the training data were used to build a neural network model (BP), random forest model (RF), a hybrid integration of particle swarm optimization and the support vector machine model (PSO-SVR), and a hybrid integration of genetic algorithm optimization and the support vector machine model (GA-SVR). Finally, the test samples were used to test the model accuracy and evaluate the generalization ability. Accordingly, the optimal prediction model for the typical shallow-buried area and near-shallow-buried area of Jurassic coal seams in northern Shaanxi was established. The results show that the HWCFZ for the typical shallow-buried coal seam is suitable to be determined by the multivariate nonlinear regression method, with an accuracy of 0.64; the HWCFZ for near-shallow-buried coal seams is suitable to be predicted by the two-factor PSO-SVR computational model of mining thickness and the burial depth, with a prediction accuracy of 0.84; and machine learning methods are more suitable for near-shallow-buried areas, dealing with small-scale data and discrete data. Full article

The effective administration of lithium-ion batteries is key to the performance and durability of electric vehicles (EVs). This systematic mapping study (SMS) thoroughly examines optimization methodologies for battery management, concentrating on the estimation of state of health (SoH), remaining useful life (RUL), and state of charge (SoC). The findings disclose various methods that boost the accuracy and reliability of SoC, including enhanced variants of the Kalman filter, machine learning models like long short-term memory (LSTM) and convolutional neural networks (CNNs), as well as hybrid optimization frameworks that combine Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). For estimating SoH, prevalent data-driven techniques include support vector regression (SVR) and Gaussian process regression (GPR), alongside hybrid models merging machine learning with conventional estimation techniques to heighten predictive accuracy. RUL prediction sees advancements through deep learning techniques, especially LSTM and gated recurrent units (GRUs), improved using algorithms such as Harris Hawks Optimization (HHO) and Adaptive Levy Flight (ALF). This study underscores the critical role of integrating advanced filtering techniques, machine learning, and optimization algorithms in developing battery management systems (BMSs) that enhance battery reliability, extend lifespan, and optimize energy management for EVs. Moreover, innovations like hybrid models and synthetic data generation using generative adversarial networks (GANs) further augment the robustness and precision of battery management strategies. This review lays out a thorough framework for future exploration and development in the optimization of EV batteries. Full article

The early detection of breast cancer is crucial for both accelerating the treatment process and preventing the spread of cancer. The accuracy of diagnosis is also significantly influenced by the experience of pathologists. Many studies have been conducted on the correct diagnosis of breast cancer to help specialists and increase the accuracy of diagnosis. This study focuses on classifying breast cancer using deep learning models, including pre-trained VGG16, MobileNet, DenseNet201, and a custom-built Convolutional Neural Network (CNN), with the final dense layer optimized via the particle swarm optimization (PSO) algorithm. The Breast Histopathology Images Dataset was used to evaluate the performance of the model, forming two datasets: one with 157,572 images at 50 × 50 × 3 (Experimental Study 1) and another with 1116 images resized to 224 × 224 × 3 (Experimental Study 2). Both original (50 × 50 × 3) and rescaled (224 × 224 × 3) images were tested. The highest success rate was obtained using the custom-built CNN model with an accuracy rate of 93.80% for experimental study 1. The MobileNet model yielded an accuracy of 95.54% for experimental study 2. The experimental results demonstrate that the proposed model exhibits promising, and superior classification accuracy compared to state-of-the-art methods across varying image sizes and dataset volumes. Full article

Particle swarm optimization (PSO), an important solving method in the field of swarm intelligence, is recognized as one of the most effective metaheuristics for addressing optimization problems. Many adaptive strategies have been developed to improve the performance of PSO. Despite these advances, a key problem lies in defining the configuration criteria of the adaptive algorithm. This study presents an adaptive variant of PSO that relies on fitness landscape analysis, particularly via ruggedness factor estimation. Our approach involves adaptively updating the cognitive and acceleration factors based on the estimation of the ruggedness factor using a machine learning-based method and a deterministic way. We tested them on global optimization functions and the feature selection problem. The proposed method gives encouraging results, outperforming native PSO in almost all instances and remaining competitive with state-of-the-art methods. Full article

According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model. This algorithm includes the introduction of a mutation operator in the gray wolf algorithm to improve the search ability of the algorithm, and, at the same time, we introduce the PSO algorithm idea. The improved fusion algorithm is used as a learning algorithm to optimize the BP neural network, and the optimized network is used to process and predict the data collected from temperature and gas sensors, which effectively improves the accuracy of fire prediction. The sensor measurements were compared with the actual values to verify the effectiveness of the GWO-PSO-optimized BP neural network model. Once experimentally verified, the improved GWO-PSO algorithm achieves a correlation coefficient R of 0.96929, a prediction accuracy rate of 96.10%, and a prediction error rate of only 3.9%, while the system monitors an accurate early warning rate of 96.07%, and the false alarm and omission rates are both less than 5%. This study can detect cotton picker fires in real time and provide timely warnings, which provides a new method for the accurate detection of fires during the field operation of cotton pickers. Full article

With the wide application of Residence Time Difference (RTD) fluxgate sensors in Unmanned Aerial Vehicle (UAV) aeromagnetic measurements, the requirements for their measurement accuracy are increasing. The core characteristics of the RTD fluxgate sensor limit its sensitivity; the high-permeability soft magnetic core is especially easily interfered with by the input noise. In this paper, based on the study of the excitation signal and input noise characteristics, the stochastic resonance is proposed to be realized by adding feedback by taking advantage of the high hysteresis loop rectangular ratio, low coercivity and bistability characteristics of the soft magnetic material core. Simulink is used to construct the sensor model of odd polynomial feedback control, and the Particle Swarm Optimization (PSO) algorithm is used to optimize the coefficients of the feedback function so that the sensor reaches a resonance state, thus reducing the noise interference and improving the sensitivity of the sensor. The simulation results show that optimizing the odd polynomial feedback coefficients with PSO enables the sensor to reach a resonance state, improving sensitivity by at least 23.5%, effectively enhancing sensor performance and laying a foundation for advancements in UAV aeromagnetic measurement technology. Full article

The wind farm layout optimization problem (WFLOP) aims to maximize wind energy utilization efficiency and mitigate energy losses caused by wake effects by optimizing the spatial layout of wind turbines. Although Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) have been widely used in WFLOP due to their discrete optimization characteristics, they still have limitations in global exploration capability and optimization depth. Meanwhile, the Differential Evolution algorithm (DE), known for its strong global optimization ability and excellent performance in handling complex nonlinear problems, is well recognized in continuous optimization issues. However, since DE was originally designed for continuous optimization scenarios, it shows insufficient adaptability under the discrete nature of WFLOP, limiting its potential advantages. In this paper, we propose a Fractional-Order Difference-driven DE Optimization Algorithm called FODE. By introducing the memory and non-local properties of fractional-order differences, FODE effectively overcomes the adaptability issues of advanced DE variants in WFLOP’s discreteness while organically applying their global optimization capabilities for complex nonlinear problems to WFLOP to achieve more efficient overall optimization performance. Experimental results show that under 10 complex wind farm conditions, FODE significantly outperforms various current state-of-the-art WFLOP algorithms including GA, PSO, and DE variants in terms of optimization performance, robustness, and applicability. Incorporating more realistic wind speed distribution and wind condition data into modeling and experiments, further enhancing the realism of WFLOP studies presented here, provides a new technical pathway for optimizing wind farm layouts. Full article

The International Maritime Organization (IMO) aims for net zero emissions in shipping by 2050. Ports, key links in the supply chain, are embracing green innovation, focusing on efficient berth and quay crane scheduling to support green port development amid limited resources. Additionally, the energy consumption and carbon emissions from the port shipping industry contribute significantly to environmental challenges and the sustainable development of ports. Therefore, reducing carbon emissions, particularly those generated during vessel berthing, has become a pressing task for the industry. The increasing complexity of berth allocation now requires compliance to vessel service standards while controlling carbon emissions. This study presents an integrated model that incorporates tidal factors into the joint optimization of berth and quay crane operations, addressing both service standards and emissions during port stays and crane activities, and further designs a PSO-GA hybrid algorithm, combining particle swarm optimization (PSO) with crossover and mutation operators from a genetic algorithm (GA), to enhance optimization accuracy and efficiency. Numerical experiments using actual data from a container terminal demonstrate the effectiveness and superiority of the PSO-GA algorithm compared to the traditional GA and PSO. The results show a reduction in total operational costs by 24.1% and carbon emissions by 15.3%, highlighting significant potential savings and environmental benefits for port operators. Furthermore, the findings reveal the critical role of tidal factors in improving berth and quay crane scheduling. The results provide decision-making support for the efficient operation and carbon emission control of green ports. Full article

This paper presents an automated method for solving the initial structure of compact, high-zoom-ratio mid-wave infrared (MWIR) zoom lenses. Using differential analysis, the focal length variation process of zoom lenses under paraxial conditions is investigated, and a model for the focal power distribution and relative motion of three movable lens groups is established. The particle swarm optimization (PSO) algorithm is introduced into the zooming process analysis, and a program is developed in MATLAB to solve for the initial structure. This algorithm integrates physical constraints from lens analysis and evaluates candidate solutions based on key design parameters, such as total lens length, zoom ratio, Petzval field curvature, and focal length at tele end. The results demonstrate that the proposed method can efficiently and accurately determine the initial structure of compact MWIR zoom lenses. Using this method, a mid-wave infrared zoom lens with a zoom ratio of 50×, a total length of less than 530 mm, and the ratio of focal length to total length approximately 2:1 was successfully designed. The design validates the effectiveness and practicality of this method in solving the initial structure of zoom lenses that meet complex design requirements. Full article

This paper investigates secrecy solutions for integrated sensing and communication (ISAC) systems, leveraging the combination of a reflecting intelligent surface (RIS) and an unmanned aerial vehicle (UAV) to introduce new degrees of freedom for enhanced system performance. Specifically, we propose a secure ISAC system supported by a UAV-mounted RIS, where an ISAC base station (BS) facilitates secure multi-user communication while simultaneously detecting potentially malicious radar targets. Our goal is to improve parameter estimation performance, measured by the Cramér–Rao bound (CRB), by jointly optimizing the UAV position, transmit beamforming, and RIS beamforming, subject to constraints including the UAV flight area, communication users’ quality of service (QoS) requirements, secure transmission demands, power budget, and RIS reflecting coefficient limits. To address this non-convex, multivariate, and coupled problem, we decompose it into three subproblems, which are solved iteratively using particle swarm optimization (PSO), semi-definite relaxation (SDR), majorization–minimization (MM), and alternating direction method of multipliers (ADMM) algorithms. Our numerical results validate the effectiveness of the proposed scheme and demonstrate the potential of employing UAV-mounted RIS in ISAC systems to enhance radar sensing capabilities. Full article