Application of quantum genetic algorithm in high noise laser image security

Man, Zhenlong; Li, Jinqing; Di, Xiaoqiang; Mu, Yining

doi:10.1007/s11801-022-1070-5

Cited by 6 publications

(8 citation statements)

References 10 publications

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“…It can be seen from Figure 1 that most of the random numbers N i (0, 1) of Gauss distribution are in the interval of [23,3], and the random numbers C i (0, 1) of Cauchy distribution are concentrated in the interval of [25,5]. Therefore, when using the random number of Gauss distribution, individuals have a greater probability to search in the range near the expected value to find more accurate results.…”

Section: Double-population Adaptive Mutation Strategymentioning

confidence: 99%

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Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

Wei

Mei

et al. 2022

Measurement and Control

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The thermal management system is one of the important assemblies that ensure the secure operation of electric vehicles (EVs). Using intelligent algorithms to optimize the control strategy of the thermal management system can reduce energy consumption under the premise of effective heat dissipation of EVs. This paper attempts to construct the control strategy of EV thermal management system by coupling the modified genetic algorithm (MGA) and support vector regression (SVR). Firstly, the double-population adaptive mutation method and a novel optimization process are adopted to obtain MGA. Afterward, the performance of MGA is verified by four benchmark functions compared with three typical algorithms, which are genetic algorithm (GA), double-population genetic algorithm (DPGA), and quantum genetic algorithm (QGA). The results demonstrate that the accuracy and stability of MGA are obviously better than the other three algorithms. Secondly, MGA is applied to modify parameters of SVR kernel function, and the accuracy of MGA-SVR algorithm is verified by the Auto-MPG and Computer Hardware data sets. The mean square deviations of the SVR algorithm test set are 0.0186 and 0.0806, respectively, and the mean square deviations of the MGA-SVR algorithm test set are 0.0099 and 0.0054, respectively, which fully shows that MGA-SVR have more accurate forecasting capabilities. Finally, the thermal management system model of EV is built by the one-dimensional simulation software KULI. Under the Chinese working condition, fan speed which meets the cooling requirements of the motor and controller is obtained from the KULI model, and the database is constructed. Then, MGA-SVR is trained by database and employed to predict fan speed under the Chinese working condition and obtain control strategy of the thermal management system. Compared with traditional control strategy, the thermal management system based on MGA-SVR control strategy can not only meet the radiating requirements, but also effectively reduce the power consumption of fans.

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Section: Double-population Adaptive Mutation Strategymentioning

confidence: 99%

“…The four typical benchmark functions are selected to compare the performance of MGA with GA, multiple-population genetic algorithm (MPGA) and quantum genetic algorithm (QGA). [24][25][26] It is proved that MGA shows much better performance in both accuracy and computational stability.…”

Section: Introductionmentioning

confidence: 99%

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

Wei

Mei

et al. 2022

Measurement and Control

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“…Information on the states of a system may be stored in a system of m -qubits. However, a quantum state collapses to a classical one upon observation [ 24 ]. For m -qubits, the representation is:

Consider a three-qubits system with three pairs of amplitudes:

The current system status may be represented by:

This allows for eight possible states of information storage inside the three-qubit machine.…”

Section: Preliminariesmentioning

confidence: 99%

“…The qubit chromosome converges to a single state and loses its distinctive feature of diversity when either

approaches 1 or 0. Therefore, it is possible for the qubit representation to have both exploratory and exploitation properties [ 24 ]. The structure of the QGA is described in Algorithm 1 [ 21 , 24 ].…”

Section: Preliminariesmentioning

confidence: 99%

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A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

Darwish,

Mhaimeed,

Elzoghabi

2023

Entropy

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The majority of the recent research on text similarity has been focused on machine learning strategies to combat the problem in the educational environment. When the originality of an idea is copied, it increases the difficulty of using a plagiarism detection system in practice, and the system fails. In cases like active-to-passive conversion, phrase structure changes, synonym substitution, and sentence reordering, the present approaches may not be adequate for plagiarism detection. In this article, semantic extraction and the quantum genetic algorithm (QGA) are integrated in a unified framework to identify idea plagiarism with the aim of enhancing the performance of existing methods in terms of detection accuracy and computational time. Semantic similarity measures, which use the WordNet database to extract semantic information, are used to capture a document’s idea. In addition, the QGA is adapted to identify the interconnected, cohesive sentences that effectively convey the source document’s main idea. QGAs are formulated using the quantum computing paradigm based on qubits and the superposition of states. By using the qubit chromosome as a representation rather than the more traditional binary, numeric, or symbolic representations, the QGA is able to express a linear superposition of solutions with the aim of increasing gene diversity. Due to its fast convergence and strong global search capacity, the QGA is well suited for a parallel structure. The proposed model has been assessed using a PAN 13-14 dataset, and the result indicates the model’s ability to achieve significant detection improvement over some of the compared models. The recommended PD model achieves an approximately 20%, 15%, and 10% increase for TPR, PPV, and F-Score compared to GA and hierarchical GA (HGA)-based PD methods, respectively. Furthermore, the accuracy rate rises by approximately 10–15% for each increase in the number of samples in the dataset.

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VeriBypasser: An automatic image verification code recognition system based on CNN

Ding,

Luo,

Lin

et al. 2023

Computer Communications

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Application of quantum genetic algorithm in high noise laser image security

Cited by 6 publications

References 10 publications

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

Control strategy research of electric vehicle thermal management system based on MGA-SVR algorithm

A Quantum Genetic Algorithm for Building a Semantic Textual Similarity Estimation Framework for Plagiarism Detection Applications

VeriBypasser: An automatic image verification code recognition system based on CNN

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