Xianning is located in the fourth pole of economic growth core area of The Central Triangle, through the location entropy analysis method we can know that, tourism industry is an advantaged industry in Xianning and th...
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ISBN:
(纸本)9783038351153
Xianning is located in the fourth pole of economic growth core area of The Central Triangle, through the location entropy analysis method we can know that, tourism industry is an advantaged industry in Xianning and the adjacent region of The Central Triangle, and have their own advantages and characteristics of tourism resources. According to the space constraint issues on function realization of advantaged tourism resources in Xianning, space breakthrough strategy based on The Central Triangle pattern has been presented.
Due to the maturation of research on chaos and secure communication, the control technology of nonlinear systems, specifically chaos synchronization, has captured the attention of numerous researchers. Focusing on the...
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Due to the maturation of research on chaos and secure communication, the control technology of nonlinear systems, specifically chaos synchronization, has captured the attention of numerous researchers. Focusing on the issues of inflexibility in the design of chaotic synchronization controllers, the need for prior synchronization of the target system structure, and noise's disruptive impact on synchronization, this paper presents solutions that enhance the practical application of chaos. Firstly, the RBF neural controller is adjusted in this paper to bolster the control precision of the chaotic system and enhance its resilience to external disturbances. Secondly, this article presents an enhanced PSO optimization algorithm for the improved RBF neural controller to improve the optimization efficiency of the controller parameters. Finally, the simulation results of the Lorenz system validate the feasibility of the proposed synchronization control scheme. Additionally, the use of chaotic synchronization in image encryption demonstrates that synchronization accuracy can fulfill the requirements of image encryption application scenarios.
This study investigates a hybrid beamfocusing method for microwave wireless power transmission (MPT). We propose an optimization algorithm to obtain an optimal coefficient of phase shifters and amplitude controllers w...
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This study investigates a hybrid beamfocusing method for microwave wireless power transmission (MPT). We propose an optimization algorithm to obtain an optimal coefficient of phase shifters and amplitude controllers with maximum RF power transfer efficiency (RF-PTE) for the hybrid beamfocusing architecture. The optimization algorithm is proposed by iteratively solving the alternative optimization problem. The algorithm is simulated by applying it to an MPT system with a transmitter and receiver composed of patch array antennas operating at 10 GHz. Additionally, we implement a test bed operating at 5.8 GHz. Through the simulations and experiments, the amplitude controllers of partially-connected hybrid beamfocusing architecture can be reduced by half compared with the fully digital beamfocusing to achieve the optimal RF-PTE. Therefore, an economical and less complex MPT system can be implemented by using the hybrid beamfocusing method.
Hyperspectral images (HSIs) containing tens to hundreds of bands can be used in various image classification tasks. However, due to the high data redundancy of the spectral information, the acquiring and analysis of H...
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Hyperspectral images (HSIs) containing tens to hundreds of bands can be used in various image classification tasks. However, due to the high data redundancy of the spectral information, the acquiring and analysis of HSIs are usually relatively time-consuming and wasteful of storage space, and therefore limit the practical application of HSIs. Selecting a subset of bands without sacrificing classification accuracy is a strategy to relieve such problems. In this letter, we present an optimization-based method, which can jointly optimize the band selection (BS) and the classification network parameters for HSIs. The proposed method regards the discrete selection problem as a continuous constrained optimization problem and adaptively selects the informative band subsets for classification. Besides, the experimental results on three public datasets show that our BS method outperforms the state-of-the-art methods in terms of classification accuracy.
Wind energy, as a widely distributed, pollution-free energy, is strongly supported by the government. Accurate wind power forecasting technology ensures the balance of the power system and enhances the security of the...
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Wind energy, as a widely distributed, pollution-free energy, is strongly supported by the government. Accurate wind power forecasting technology ensures the balance of the power system and enhances the security of the system. In this paper, a wind power prediction model with the improved long short-term memory (LSTM) network and Adaboost algorithm was constructed based on the mismatch of data and power climb. This method was based on mutual information (MI) and power division (PD), named MI-PD-AdaBoost-LSTM. MI was used for quantifying the time delay between variables and power. Furthermore, to solve the relationship between wind speed and power in different weather fluctuation processes, the method of power fluctuation process division was proposed. Moreover, the asymmetric loss function of AdaBoost-LSTM was constructed to deal with the asymmetric characteristics of wind power. An improved artificial bee colony (ABC) algorithm, which overcame the local optimal problem, was used to optimize the asymmetric loss function parameters. Finally, the performance of different deep learning prediction models and the proposed prediction model was analyzed in the experiment. Numerical simulations showed that the proposed algorithm effectively improves the power prediction accuracy with different time scales and seasons. The designed model provides guidance for wind farm power prediction.
This research introduces a new composite system that utilizes multiple moving masses to identify cracks in structures resembling beams. The process starts by recording displacement time data from a set of these moving...
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This research introduces a new composite system that utilizes multiple moving masses to identify cracks in structures resembling beams. The process starts by recording displacement time data from a set of these moving masses and converting this information into a relative time history through weighted aggregation. This relative time history then undergoes wavelet transform analysis to precisely locate cracks. Following wavelet examinations, specific points along the beam are determined as potential crack sites. These points, along with locations on the beam susceptible to cracked point due to support conditions, are marked as crack locations within the optimization algorithm's search domain. The model uses equations of motion based on the finite element method for the moving masses on the beam and employs the Runge-Kutta numerical solution within the state space. The proposed system consists of three successive moving masses positioned at even intervals along the beam. To assess its effectiveness, the method is tested on two examples: a simply supported beam and a continuous beam, each having three scenarios to simulate the presence of one or multiple cracks. Additionally, another example investigates the influence of mass speed, spacing between masses, and noise effect. The outcomes showcase the method's effectiveness and efficiency in localizing crack, even in the presence of noise effect in 1%, 5% and 20%.
Recently, negative sequential patterns (NSP) (like missing medical treatments) mining is important in data mining research since it includes negative correlations between item sets, which are overlooked by positive se...
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Recently, negative sequential patterns (NSP) (like missing medical treatments) mining is important in data mining research since it includes negative correlations between item sets, which are overlooked by positive sequential pattern mining (PSP) (for instance, utilization of medical service). Yet, discovering the NSP is very complex than finding PSP because of the important problem complexity occurred by high computational cost, non-occurring elements, as well as huge search space in evaluating NSC, and most of the NSP based existing works are inefficient. Therefore, this paper intends to propose a fast NSP mining algorithm for the disease prediction model. This model includes Data normalization, Data separation based on labels, and Pattern recognition phases. In the midst of data separation, the maximum occurring data is optimally selected using a new algorithm that hybridizes the FireFly (FF) algorithm and Grey Wolf optimization (GWO). This proposed Firefly induced Grey Wolf optimization (F-GWO) algorithm automatically selects the maximum occurring information as per the PSP support. The proposed model is compared over other conventional methods with varied measures. Especially, the computation cost of our model is 46.87%, 6.27%, 9.37%, 2.76%, and 66.62% better than the existing GA, ABC, PSO, FF, and GWO models respectively.
An optimization algorithm for planning the motion of a humanoid robot during extravehicular activities is presented in this paper. The algorithm can schedule and plan the movements of the two robotic arms to move the ...
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An optimization algorithm for planning the motion of a humanoid robot during extravehicular activities is presented in this paper. The algorithm can schedule and plan the movements of the two robotic arms to move the humanoid robot by using the handrails present outside the International Space Station. The optimization algorithm considers the eventual constraints imposed by the topology of the handrails and calculates the sequence of grasping and nongrasping phases needed to push and pull the robot along the handrails. A low-level controller is also developed and used to track the planned arms and end-effectors trajectories. Numerical simulations assess the applicability of the proposed strategy in three different typical operations that potentially can be performed in an extravehicular activity scenario.
In the recent years, many heuristic optimization algorithms have been developed. A majority of these heuristic algorithms have been derived from the behavior of biological or physical systems in nature. In this paper,...
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ISBN:
(纸本)9781479963874
In the recent years, many heuristic optimization algorithms have been developed. A majority of these heuristic algorithms have been derived from the behavior of biological or physical systems in nature. In this paper, we propose a new optimization algorithm based on competitive behavior of animal groups. In the proposed algorithm, the whole population is divided into a number of groups. In each group, the best searching agent spreads its children in its owned territory. Any group which is not able to find rich resources will be eliminated form competition. The competition gradually results in an increase in population of wealthy group which gives a fast convergence to proposed optimization algorithm. In the following, after a detailed explanation of the algorithm and pseudo code, we compare it to other existing algorithms, including genetics and particle swarm optimizations. Applying the proposed algorithm on various benchmark cost functions, shows faster and superior results compared to other optimization algorithms.
With the fast evolution of Internet of Things (IoT) applications, Wireless Sensor Networks (WSNs) have become a crucial part of modern infrastructure. The efficient provision of services and wiser use of resources are...
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With the fast evolution of Internet of Things (IoT) applications, Wireless Sensor Networks (WSNs) have become a crucial part of modern infrastructure. The efficient provision of services and wiser use of resources are currently of great importance. WSNs consist of several sensor nodes that collaborate to monitor and send data to a central location known as a sink. The sink, also called a base station, serves as the endpoint for data transmission in each round. However, due to the limited computation, storage, and energy resources of sensor nodes, they often face challenges in changing clusters. Optimal selection of a node, aimed at minimizing network fragmentation and enhancing energy utilization, necessitates a sophisticated evaluation and computational procedure, demanding a substantial energy investment. Subsequently, the task at hand is the development of a system facilitating the connection of remote sensing sources to WSNs with minimal energy consumption. The primary goals of WSNs based on the IoT revolve around extending network longevity and enhancing energy efficiency. In the realm of IoT-based WSNs, where the efficiency of data collection and management is paramount, cluster-based methodologies have demonstrated their effectiveness. This investigation proposes the implementation of a most valuable player algorithm (MVPA) specifically tailored for IoT-based WSNs, taking into account diverse factors influencing node energy and network lifespan. The MVPA is a highly competitive optimization method that converges faster (with fewer function evaluations) and has a greater overall success rate. In this case, the optimum cluster head for an IoT-based WSN was chosen using an MVPA to maximize energy savings. Simulation results demonstrate that the recommended strategy, when compared to other current methods, increases the network lifetime by using the minimum amount of energy needed to function.
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