Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, med-ical experts, and medical centers over public networks, which require high levels of security to protect the content ...
详细信息
Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, med-ical experts, and medical centers over public networks, which require high levels of security to protect the content of medical images and the personal information they contain. In this paper, we propose a new stego image encryption scheme based on a new secret image compression method, wavelet trans-formation, QR decomposition of the cover image, and a new chaotic map. The secret image is compressed by the Hahn-Krawtchouk hybrid quaternion square moments (HK-HQSM), which are optimized by a new hybrid metaheuristic algorithm based on the Salp Swarm algorithm (SSA) and the Arithmetic optimization algorithm (AOA). To increase the security level when transmitting the proposed stego im-ages over public networks, we introduce a new chaotic map based on the 2D fractional Henon map to encrypt the stego image. To demonstrate the effectiveness of the proposed steganography scheme for IoMT, we implemented this scheme on a low-cost Raspberry Pi 4 hardware board. The results of the per-formed numerical experiments show that our method is secure and provides exceptional robustness against common standard image processing attacks (steganalysis attacks). The results also demonstrate that our strategy is able to work efficiently and quickly when implemented on a Raspberry Pi board.& COPY;2023 The Author(s). Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids...
详细信息
E-mobility is a key element in the future energy systems. The capabilities of EVs are many and vary since they can provide valuable system flexibility services, including management of congestion in transmission grids. According to the literature, leaving the charging process uncontrolled could hinder some of the present challenges in the power system. The development of a suitable charging management system is required to address different stakeholders' needs in the electro-mobility value chain. This paper focuses on the design of such a system, the TwinEV module, that offers high-value services to electric vehicles (EV) users. This module is based on a Smart Charging Tool (SCT), aiming to deliver a more user-central and cooperative approach to the EV charging processes. The methodology of the SCT tool, as well as the supportive optimization algorithm, are explained thoroughly. The architecture and the web applications of TwinEV module are analyzed. Finally, the deployment and testing results are presented.
The graph coloring is a classic NP-complete problem. Presently there is no effective method to solve this problem. Here we propose a modifled particle swarm optimization (PSO) algorithm in which a disturbance factor i...
详细信息
The graph coloring is a classic NP-complete problem. Presently there is no effective method to solve this problem. Here we propose a modifled particle swarm optimization (PSO) algorithm in which a disturbance factor is added to a particle swarm optimizer for improv- ing its performance. When the current global best solution cannot be updated in a certain time period that is longer than the disturbance factor, a certain number of particles will be chosen according to probability and their velocities will be reset to force the particle swarm to get rid of local minimizers. It is found that this operation is helpful to improve the performance of particle swarm. Classic planar graph coloring problem is resolved by using modifled particle swarm optimization algorithm. Numerical simulation results show that the per- formance of the modified PSO is superior to that of the classical PSO.
The aim of the present study was to test the four commonly used models to predict the dates of flowering of temperate-zone trees, the spring warming, sequential, parallel and alternating models. Previous studies conce...
详细信息
The aim of the present study was to test the four commonly used models to predict the dates of flowering of temperate-zone trees, the spring warming, sequential, parallel and alternating models. Previous studies concerning the performance of these models have shown that they were unable to make accurate predictions based on external data. One of the reasons for such inaccuracy may be wrong estimations of the parameters of each model due to the non-convergence of the optimization algorithm towards their maximum likelihood. We proposed to fit these four models using a simulated annealing method which is known to avoid local extrema of any kind of function, and thus is particularly well adapted to fit budburst models, as their likelihood function presents many local maxima. We tested this method using a phenological dataset deduced from aero-palynological data. Annual pollen spectra were used to estimate the dates of flowering of the populations around the sampling station. The results show that simulated annealing provides a better fit than traditional methods, Despite this improvement, classical models still failed to predict external data. We expect the simulated annealing method to allow reliable comparisons among models, leading to a selection of biologically relevant ones.
With the continuous acceleration of the global construction industry, many structural infrastructure structures in China have been put into use for decades. They are very prone to damage due to fatigue. The modal para...
详细信息
With the continuous acceleration of the global construction industry, many structural infrastructure structures in China have been put into use for decades. They are very prone to damage due to fatigue. The modal parameter identification of civil engineering construction can evaluate the safety status of infrastructure structures. In view of this, an identification system of civil engineering structure modal parameters is proposed based on improved wavelet transform. In the process, the mode shape was chosen as the method of wavelet transform. The data was discretized by selecting the actual data of a high-speed railway station combined with sensors and wavelet transform. Finally, the correct identification of the modal parameters of civil engineering structures is realized. The data shows that under normal conditions where there is only white noise interference, the waveform of the structure is relatively stable, and the amplitude fluctuation is in the [-2,3] interval. At the same time, the average amplitude of the structure is in the [2.2, -1.5] interval under normal conditions. In addition, the positive and negative extreme points are 3.7 and -2.3, respectively. This indicates that the structure amplitude fluctuation is in a dynamic and stable state under normal circumstances. The optimized wavelet transform method identifies a total of four orders in the first six natural frequencies. The minimum error is 0.11%, the maximum error is 1.50%, and the average error of the first four natural frequencies is 0.578%. In addition, based on the comparison of theoretical and identification values of longitudinal vibration shapes, the proposed method can successfully detect abnormal values at the 10th and 18th nodes. From the above results, it shows that the wavelet transform method has high accuracy and small error in frequency identification. It meets the requirements of identifying the natural frequency parameters of the structure. From the calculation, the method propos
Group decision-making management is an important issue in water management reform and development. The lacking of effective communication and cooperation is the major defects of the existing group decision-making mode...
详细信息
Group decision-making management is an important issue in water management reform and development. The lacking of effective communication and cooperation is the major defects of the existing group decision-making models. Based on the in-depth analysis of the coordinating characteristic in group decision making, this study proposed a multi-layer dynamic model of water resource allocation and scheduling. This model focuses on effective communication and coordination. In order to solve the problem of poor convergence of multi-round decision-making process in water resource allocation and scheduling, the scheme-recognized cooperative satisfaction index and scheme-adjusted rationality index are introduced. An optimization algorithm based on the effective distance of group utility is proposed, which can solve the problem about coordination of limited resources-based group decision-making process. The simulation results show that the proposed model has better convergence than the existing models.
In a three-dimensional (3-D) model-based objects tracking and recognition system, the key problem of objects location is to establish the relationship between 2-D objects image and 3-D model. Based on 3-D model projec...
详细信息
ISBN:
(纸本)9781424427239
In a three-dimensional (3-D) model-based objects tracking and recognition system, the key problem of objects location is to establish the relationship between 2-D objects image and 3-D model. Based on 3-D model projection and 2-D image feature extraction, a modified Hausdorff distance is used to establish the matching function. The relationship between matching parameters are described with a probability model, and the distribution of parameter evolves towards the direction of dominant character through probability model learning and the corresponding operation, which is proposed to solve the problem of overmany iteration and slow constringency velocity The experiments show that the optimal matching parameters between 3-D model and 2-D image feature can be found accurately and efficiently, and then the accurate object location is completed.
This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based opti...
详细信息
This study presents an application of the self-organizing migrating algorithm (SOMA) to train artificial neural networks for skin segmentation tasks. We compare the performance of SOMA with popular gradient-based optimization methods such as ADAM and SGDM, as well as with another evolutionary algorithm, differential evolution (DE). Experiments are conducted on the skin dataset, which consists of 245,057 samples with skin and non-skin labels. The results show that the neural network trained by SOMA achieves the highest accuracy (93.18%), outperforming ADAM (84.87%), SGDM (84.79%), and DE (91.32%). The visual evaluation also reveals the SOMA-trained neural network's accurate and reliable segmentation capabilities in most cases. These findings highlight the potential of incorporating evolutionary optimization algorithms like SOMA into the training process of artificial neural networks, significantly improving performance in image segmentation tasks.
In order to improve the precision of attitude operator in GPS attitude determination, based on Quantum-behaved Particle Swarm optimization(QPSO) algorithm, a new GPS carrier phase searching technology of attitude dete...
详细信息
ISBN:
(纸本)9781424421138
In order to improve the precision of attitude operator in GPS attitude determination, based on Quantum-behaved Particle Swarm optimization(QPSO) algorithm, a new GPS carrier phase searching technology of attitude determination is proposed. In favor of the ambiguity function method's fitness function, quantum behavior is introduced to enhance the ability of global searching to achieve the GPS fast determination. The simulations show the QPSO algorithm applied to solve benchmark functions is stable, fast of the searching speed and have a high accuracy. The actual application shows the method used in GPS attitude operator based on QPSO algorithm is able to search in the complex space, and the precision is high, the speed is rapid and the application effect is notable.
暂无评论