Performance Comparison Of Evolutionary Techniques Enhanced By Lozi Chaotic Map In The Task Of Reactor Geometry Optimization

2014 ◽  
Author(s):  
Michal Pluhacek ◽  
Roman Senkerik ◽  
Ivan Zelinka ◽  
Donald Davendra
Keyword(s):  
Chaotic Map ◽  
Geometry Optimization ◽  
Performance Comparison ◽  
Reactor Geometry

scholarly journals S-Box Construction Method Based on the Combination of Quantum Chaos and PWLCM Chaotic Map

2021 ◽  
Vol 15 (4) ◽  
pp. 1-17
Author(s):  
Jun Peng ◽  
Shangzhu Jin ◽  
Shaoning Pang ◽  
Du Zhang ◽  
Lixiao Feng ◽  
...  
Keyword(s):  
Chaotic System ◽  
Quantum Chaos ◽  
Chaotic Systems ◽  
User Authentication ◽  
Chaotic Map ◽  
Data Encryption ◽  
Performance Comparison ◽  
Generation Process ◽  
Secret Key ◽  
Differential Approximation

For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis. In this article, we incorporate quantum chaos and PWLCM chaotic map into a new method of S-box design. The secret key is transformed to generate a six tuple system parameter, which is involved in the generation process of chaotic sequences of two chaotic systems. The output of one chaotic system will disturb the parameters of another chaotic system in order to improve the complexity of encryption sequence. S-box is obtained by XOR operation of the output of two chaotic systems. Over the obtained 500 key-dependent S-boxes, we test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, respectively. Performance comparison of proposed S-box with those chaos-based one in the literature has been made. The results show that the cryptographic characteristics of proposed S-box has met our design objectives and can be applied to data encryption, user authentication and system access control.

scholarly journals Secure and Efficient Image Compression-Encryption Scheme Using New Chaotic Structure and Compressive Sensing

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yongli Tang ◽  
Mingjie Zhao ◽  
Lixiang Li
Keyword(s):  
Image Compression ◽  
Information Transmission ◽  
Compressive Sensing ◽  
Chaotic System ◽  
Rapid Development ◽  
Chaotic Map ◽  
Performance Comparison ◽  
Encryption Scheme ◽  
The Matrix ◽  
Brute Force Attack

The rapid development of the Internet leads to a surge in the amount of information transmission and brings many security problems. For multimedia information transmission, especially digital images, it is necessary to compress and encrypt at the same time. The emergence of compressive sensing solves this problem. Compressive sensing can compress and encrypt at the same time, which can not only reduce the transmission bandwidth of the network but also improve the security of the system. However, when using compressive sensing encryption, the whole measurement matrix needs to be stored, and the compressive sensing can be combined with a chaotic system, so only the generation parameters of the matrix need to be stored, and the security of the system can be further improved by using the sensitivity of the chaotic system. This paper introduces a secure and efficient image compression-encryption scheme using a new chaotic structure and compressive sensing. The chaotic map used in the scheme is generated by our new and universal chaotic structure, which not only expands the chaotic range of the chaotic system but also improves the performance of the chaotic system. After analyzing the performance comparison of traditional one-dimensional chaotic maps and some existing methods, the image compression-encryption scheme based on a new chaotic structure and compressive sensing has a good encryption effect and large keyspace, which can resist brute force attack and statistical attack.

scholarly journals NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

2021 ◽  
Author(s):  
Yifan Wang ◽  
Tai-Ying Chen ◽  
Dionisios Vlachos
Keyword(s):  
Active Learning ◽  
Geometry Optimization ◽  
Optimization Theory ◽  
Computational Design ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Condition Optimization ◽  
Reactor Geometry

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

scholarly journals S-box Construction Method Based on the Combination of Quantum Chaos and PWLCM Chaotic Map

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Chaotic System ◽  
Quantum Chaos ◽  
Chaotic Systems ◽  
User Authentication ◽  
Chaotic Map ◽  
Data Encryption ◽  
Performance Comparison ◽  
Generation Process ◽  
Secret Key ◽  
Differential Approximation

For a security system built on symmetric-key cryptography algorithms, the substitution box (S-box) plays a crucial role to resist cryptanalysis. In this article, we incorporate quantum chaos and PWLCM chaotic map into a new method of S-box design. The secret key is transformed to generate a six tuple system parameter, which is involved in the generation process of chaotic sequences of two chaotic systems. The output of one chaotic system will disturb the parameters of another chaotic system in order to improve the complexity of encryption sequence. S-box is obtained by XOR operation of the output of two chaotic systems. Over the obtained 500 key-dependent S-boxes, we test the S-box cryptographical properties on bijection, nonlinearity, SAC, BIC, differential approximation probability, respectively. Performance comparison of proposed S-box with those chaos-based one in the literature has been made. The results show that the cryptographic characteristics of proposed S-box has met our design objectives and can be applied to data encryption, user authentication and system access control.

scholarly journals NEXTorch: A Design and Bayesian Optimization Toolkit for Chemical Sciences and Engineering

2021 ◽  
Author(s):  
Yifan Wang ◽  
Tai-Ying Chen ◽  
Dionisios Vlachos
Keyword(s):  
Active Learning ◽  
Geometry Optimization ◽  
Optimization Theory ◽  
Computational Design ◽  
Bayesian Optimization ◽  
Optimization Approach ◽  
Loop Optimization ◽  
Human In The Loop ◽  
Condition Optimization ◽  
Reactor Geometry

<div> <div> <div> <p>Automation and optimization of chemical systems require well-inform decisions on what experiments to run to reduce time, materials, and/or computations. Data-driven active learning algorithms have emerged as valuable tools to solve such tasks. Bayesian optimization, a sequential global optimization approach, is a popular active-learning framework. Past studies have demonstrated its efficiency in solving chemistry and engineering problems. We introduce NEXTorch, a library in Python/PyTorch, to facilitate laboratory or computational design using Bayesian optimization. NEXTorch offers fast predictive modeling, flexible optimization loops, visualization capabilities, easy interfacing with legacy software, and multiple types of parameters and data type conversions. It provides GPU acceleration, parallelization, and state-of-the-art Bayesian Optimization algorithms and supports both automated and human-in-the-loop optimization. The comprehensive online documentation introduces Bayesian optimization theory and several examples from catalyst synthesis, reaction condition optimization, parameter estimation, and reactor geometry optimization. NEXTorch is open-source and available on GitHub. </p> </div> </div> </div>

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