Chaos Map Research Articles

Chaotic map-based selective image encryption via diffusion and confusion

Industrial diversification and Technology have a drastic impact on the day-to-day life and economic growth of the country. Sharing multimedia content over the network has drastically increased. The multimedia data can be just social media images or some highly confidential data. There is a high demand for secure image transmission over networks in the age of the Internet and other transmission mediums. Since images are high-volume data, care must be taken to ensure that the security does not become computationally very expensive. To overcome these problems, we propose a method to encrypt the images selectively using a chaos map. Selective encryption is the process of encrypting an image so that only a piece of it is visible while the remainder is encrypted. This decreases the computational cost of the algorithm to a great extent. Selective encryption consists of two parts, public and private. The public part of the image is visible to everyone but the private part is only visible to the authorized user. The chaotic map is used for encrypting the image and providing security to the algorithm. Confusion and diffusion are used for key generation using pixel value substitution and scrambling. We use PSNR, histogram analysis, and correlation coefficient to verify the result.

Characterisation of chaos and mean-motion resonances in meteoroid streams

Context. Dynamically linking a meteor shower with its parent body is challenging, and chaos in the dynamics of meteoroid streams may contribute to this challenge. For a robust identification of parent bodies, it is therefore necessary to quantify the amount of chaos involved in the evolution of meteoroid streams. Aims. Characterising chaos in meteoroid streams through the aid of chaos maps is still a new field of study. Thus, we examine two very different meteoroid streams, the Draconids and the Leonids, in order to obtain a general view of this topic. Methods. We used the method developed in a previous paper dedicated to Geminids, drawing chaos maps with the orthogonal fast Lyapunov indicator. We chose four particle size ranges to investigate the effect of non-gravitational forces. As the dynamics are structured by mean-motion resonances with planets, we computed the locations and widths of the resonances at play. We used semi-analytical formulas valid for any eccentricity and inclination and an arbitrary number of planets. Results. We pinpoint which mean-motion resonances with Jupiter play a major role in the dynamics of each meteoroid stream. We show how those resonances tend to trap mostly large particles, preventing them from meeting with Jupiter. We also study particles that manage to escape those resonances, for example, due to the gravitational perturbation of Saturn. Finally, we explain why non-gravitational forces do not disturb the dynamics much, contrary to what is observed for the Geminids.

Improve the Hunger Games search algorithm to optimize the GoogleNet model.

The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%.

Efficient Image Encryption via 2D Logistic Chaos Mapping: Strengthening Security with Pixel-Level Dynamics

This study presents an innovative image encryption algorithm anchored on the two-dimensional Logistic chaotic system, designed to address the limitations of traditional methods in terms of speed and security. The algorithm employs pixel shifting and scrambling, coupled with a segmented grayscale value substitution, to achieve a high level of security with strong key sensitivity and robust resistance to both differential and statistical attacks. Experimental validation demonstrates its superiority with fast encryption speeds, as evidenced by an encryption time of 0.2136 seconds for a 256×256 color image, and an entropy close to its ideal value, indicating excellent randomness. This method also exhibits high performance in resisting differential attacks, with Number of Pixels Change Rate (NPCR) and the Unified Average Changing Intensity (UACI) values nearing their optimal expected values. The integration of Quantum Key Distribution (QKD) is suggested for future work to enhance security against quantum computing threats, solidifying the algorithm’s potential as a leading solution for the image encryption.

Design A Novel Image Encryption Algorithm Based on A PRNG with High Performance

This paper proposes a novel two-dimensional logistic map (2D-LM). Relative to the classical 2D logistic modulation map (2D-LMM), it has larger chaotic range and better chaos performance. In comparison with some 2D chaos maps, the proposed new 2D-LM also has unique advantages in the complexity of sequence. To further study its application, the novel image encryption algorithm is designed based on the proposed 2D-LM. The security tests show the higher anti-decryption ability of the design algorithm. Finally, we develop a FPGA test platform to realize the Pseudo-Random Number Generator (PRNG) based on the proposed 2D-LM. The experiment results show the feasibility of its industrial applications.

Various pseudo random number generators based on memristive chaos map model

Various pseudo random number generators based on memristive chaos map model

Fault Feature Extraction of Parallel-Axis Gearbox Based on IDBO-VMD and t-SNE

For the problem that the fault states of parallel shaft gearboxes are difficult to identify, a diagnostic method is proposed to optimize variational modal decomposition (VMD) and t-distributed stochastic neighbor embedding (t-SNE) using an improved dung beetle optimization algorithm I have checked and revised all. (IDBO). IDBO is obtained by amplifying dung beetle optimization (DBO) using strategies such as chaos mapping, Levy flight policy, and dynamic adaptive weighting. IDBO is employed to optimize VMD, extracting decomposed eigenvalues restructured into high-dimensional feature vectors. Subsequently, we employ the t-SNE algorithm for dimensionality reduction to eliminate redundancy, obtaining two-dimensional vectors. Finally, these vectors are input into a support vector machine (SVM) for fault diagnosis. We apply IDBO, grey wolf optimization (GWO), DBO, and the sparrow search algorithm (SSA) to both benchmark functions and VMD, conducting a performance comparison. The results demonstrate that IDBO exhibits superior convergence speed and global search capability, effectively suppressing modal aliasing issues in VMD, thereby enhancing the algorithm’s robustness. Through experimental fault diagnosis on a gear transmission system, we compare our proposed method with EMD + t-SNE and traditional VMD + t-SNE feature extraction approaches. The experimental results indicate that the fault diagnosis accuracy reaches 100% after processing the fault signals with IDBO-VMD + t-SNE. This method proves to be an effective fault diagnosis approach specifically tailored for parallel-axis gearboxes, providing a reliable means to enhance diagnostic accuracy.

A Multi-Strategy Improved Sooty Tern Optimization Algorithm for Concrete Dam Parameter Inversion

A original strategy for optimizing the inversion of concrete dam parameters based on the multi-strategy improved Sooty Tern Optimization algorithm (MSSTOA) is proposed to address the issues of low efficiency, low accuracy, and poor optimizing performance. First, computational strategies to improve the traditional Sooty tern algorithm, such as chaos mapping to improve the initial position of the population, a new nonlinear convergence factor, the LIMIT threshold method, and Gaussian perturbation to update the optimal individual position, are adopted to enhance its algorithmic optimization seeking ability. Then, the measured and finite element data are combined to create the optimization inversion fitness function. Based on the MSSTOA, the intelligent optimization inversion model is constructed, the inversion efficiency is improved by parallel strategy, and the optimal parameter inversion is searched. The inversion strategy is validated through test functions, hypothetical arithmetic examples, and concrete dam engineering examples and compared with the inversion results of the traditional STOA and other optimization algorithms. The results show that the MSSTOA is feasible and practical, the test function optimization results and computational time are better than the STOA and other algorithms, the example inversion of the elastic modulus is more accurate than the traditional STOA calculation, and the results of the MSSSTOA inversion are reasonable in the engineering example. Compared with other algorithms, the local extremes are skipped, and the time consumption is reduced by at least 48%. The finite element hydrostatic components calculated from the inversion results are well-fitted to the statistical model with minor errors. The intelligent inversion strategy has good application in concrete dam inverse analysis.

Optimization of Food Processing Parameters Based on Parametric Models

Abstract In this paper, a food processing process optimization model is constructed based on the improved particle swarm algorithm. By adding the chaos mapping strategy to the basic particle swarm algorithm, the optimization efficiency of the particle swarm under the constraints is improved. Combining the multi-objective optimization capability of the NDWPSO algorithm, the process parameter optimization process is constructed to optimize the parameters of food processing processes. The proposed method for process parameter optimization is applied to the vacuum drying process parameter optimization experiment of Jujube to test the effectiveness of particle swarm process parameter optimization. The results show that the diversity value and the best-adapted value of the NDWPSO algorithm in the single-peak function only use a small number of iterations to drop to a level close to 0, indicating that the NDWPSO algorithm has a faster convergence speed. In the vacuum drying process of jujube, the theoretical values of the optimal process parameters for freeze-dried jujube slices are: jujube slices thickness 5mm, sublimation drying temperature -22℃, resolution drying temperature 20℃.

Minimality and distributional chaos in triangular maps

The result of this paper contributes to the classification of triangular maps of the square with zero topological entropy stated by A. N. Sharkovsky in the 1980s. The problem was if a triangular map of the square such that its any ω-limit set contains unique minimal set can be distributionally chaotic. So far such result was disproved only for the class of triangular maps non-decreasing on fibres [L. Paganoni, J. Smítal, Strange distributionally chaotic triangular maps, Chaos Solitons Fractals 26(2) (2005), pp. 581–589]. In this paper, we solve the problem in negative for all triangular maps of the square, correcting the original result from Balibrea and Smítal [Strong distributional chaos and minimal sets, Topology appl. 156 (2009), pp. 1673–1678].

Lossless Image Encryption using Robust Chaos-based Dynamic DNA Coding, XORing and Complementing

In this paper, we present a lossless image encryption algorithm utilizing robust chaos based dynamic DNA coding and DNA operations (DNA XOR and DNA Complement). The entire process of encryption is controlled by the pseudo-random number sequences generated through a 1D robust chaos map that exhibits chaotic behaviour in a very large region of parameter space with no apparent periodic window and therefore possesses a fairly large key space. Due to peculiar feed-forward and feedback mechanisms, which modify the synthetic image (created to initiate the encryption process) at the encryption of each pixel, the proposed algorithm possesses extreme sensitivity to the plain image, cipher image and secret key. The performance analysis proves that the proposed algorithm exhibits excellent features (as expected from ideal image encryption algorithms) and is robust against various statistical and cryptanalytic attacks.

Human Evolutionary Optimization Algorithm

This paper introduces the Human Evolutionary Optimization Algorithm (HEOA), a metaheuristic algorithm inspired by human evolution. HEOA divides the global search process into two distinct phases: human exploration and human development. Logistic Chaos Mapping is employed for initialization. In the human exploration phase, an initial global search is conducted, followed by the human development phase, in which the population is categorized into leaders, explorers, followers, and losers, each utilizing distinct search strategies. The convergence speed and search accuracy of HEOA are evaluated using 23 well-established test functions. Furthermore, the algorithm's applicability in engineering optimization is assessed with four engineering problems. A comparative analysis with ten other algorithms highlights HEOA's effectiveness, as evidenced by various performance metrics and statistical measures. Consistently, the results demonstrate that HEOA surpasses most current state-of-the-art algorithms in approximating optimal solutions for complex global optimization problems. The MATLAB code for HEOA is available at https://github.com/junbolian/HEOA.git.

Automated rail-water intermodal transport container terminal handling equipment cooperative scheduling based on bidirectional hybrid flow-shop scheduling problem

The importance of rail-water intermodal transport is becoming more and more prominent in recent years. As a key node of rail-water intermodal transportation, the operation efficiency of automated container terminal directly affects the overall transportation efficiency. However, there are few studies on the collaborative scheduling of automated container terminal equipment in rail-water intermodal transportation. Therefore, this paper focuses on the ‘Automated Quay Crane (AQC)-Automated Straddle Carrier (ASC)-Automated Rail Mounted Gantry Crane (ARMG)’ operation system and addresses the operational efficiency requirements of the Automated Rail-Water Intermodal Transport Container Terminal (ARWITCT), Based on the bidirectional Hybrid Flow-shop Scheduling Problem (HFSP), a scheduling optimization model for the ARWITCT is established with the objective of minimizing the maximum completion time. To solve this complex scheduling problem, Particle Swarm Optimization based on Adaptive Tent Chaos Mapping (PSO-ATCM) is designed. In this algorithm, a parameter adaptive adjustment strategy and a Tent Chaos Mapping Optimization Strategy are introduced to help PSO jump out of local optimum. Problem-feature-based encoding and decoding methods are designed for the problem. An ‘insertion-translation’ mechanism is used to judge and resolve the conflicts between import and export containers during the decoding process. The effect of proposed scheduling model and the performance of the proposed algorithm are validated via comprehensive comparison experiments. To validate the effectiveness of proposed scheduling model, we compare the unidirectional HFSP model with the bidirectional HFSP model. The proposed PSO-ATCM is compared with standard Genetic Algorithm, Particle Swarm Optimization algorithm and Adaptive Genetic Algorithm. Computational results demonstrate that the proposed model is effective and PSO-ATCM performs better than other three compared algorithms.

Optimal location using genetic algorithms for chaotic image steganography technique based on discrete framelet transform

The development of network technology and its use have led society into the internet world and communication through digital data. The ease of access to digital resources and the possibility of unauthorized copies and distribution of them have gave rise to the significant challenge of protecting the ownership of these data. In this context, steganography is cited as a possible solution to some of these problems. Steganography can be understood as the practice of hiding secret information within another object, as for instance a text, image or video, such that the presence of this hidden information is not evident and it can be extracted if necessary. In this paper we provide an image steganography algorithm in order to insert a grayscale secret image into a RGB color cover image. This algorithm is based on a general hybrid chaos map and a 3D shift function applied to the pixels of a color image. In order to select suitable hiding places in the host image, we develop a genetic algorithm with permutation encoding aiming to optimize this task. We demonstrate empirically the effectiveness of the proposed method from various perspectives, including transparency, security, and resistance.

MSXFGP: combining improved sparrow search algorithm with XGBoost for enhanced genomic prediction

BackgroundWith the significant reduction in the cost of high-throughput sequencing technology, genomic selection technology has been rapidly developed in the field of plant breeding. Although numerous genomic selection methods have been proposed by researchers, the existing genomic selection methods still face the problem of poor prediction accuracy in practical applications.ResultsThis paper proposes a genome prediction method MSXFGP based on a multi-strategy improved sparrow search algorithm (SSA) to optimize XGBoost parameters and feature selection. Firstly, logistic chaos mapping, elite learning, adaptive parameter adjustment, Levy flight, and an early stop strategy are incorporated into the SSA. This integration serves to enhance the global and local search capabilities of the algorithm, thereby improving its convergence accuracy and stability. Subsequently, the improved SSA is utilized to concurrently optimize XGBoost parameters and feature selection, leading to the establishment of a new genomic selection method, MSXFGP. Utilizing both the coefficient of determination R2 and the Pearson correlation coefficient as evaluation metrics, MSXFGP was evaluated against six existing genomic selection models across six datasets. The findings reveal that MSXFGP prediction accuracy is comparable or better than existing widely used genomic selection methods, and it exhibits better accuracy when R2 is utilized as an assessment metric. Additionally, this research provides a user-friendly Python utility designed to aid breeders in the effective application of this innovative method. MSXFGP is accessible at https://github.com/DIBreeding/MSXFGP.ConclusionsThe experimental results show that the prediction accuracy of MSXFGP is comparable or better than existing genome selection methods, providing a new approach for plant genome selection.

ISCSO-PTCN-BIGRU Prediction Model for Fracture Risk Grade of Gas-Containing Coal Fracture

A multi-strategy improved sand cat swarm algorithm with PTCN-BIGRU is proposed to solve the problem of predicting the risk level of gas-containing coal fracture. Combined with kernel entropy component analysis to downscale the gas-containing coal fracture risk level predictors, TCN is used for feature extraction by parallel convolution operation, and BiGRU is used to further obtain the contextual links of the features. A parameterized exponential linear unit based on the standard TCN is used to improve the linear unit and to enhance the generalization capability of the model. Combined with the sand cat swarm optimization algorithm to determine the optimal BIGRU network parameters, Singer chaos mapping, chaos decreasing factor, and adaptive t-distribution are used to improve the SCSO for optimal risk level prediction accuracy. The results show that the prediction accuracy of the ISCSO-PTCN-BiGRU model is 93.33%, which is better than other models, and it is proved that this paper can effectively improve the prediction accuracy of gas-containing coal fracture risk level. This research adds a theoretical support for the prevention of gas protrusion accidents and a guarantee for the safety of underground production in coal mines.

Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot

Industrial robot speed control remains a critical aspect for efficient operations, especially given the challenges of nonlinearity and multivariable characteristics inherent to servo motor control systems, as well as energy inefficiencies due to a lack of automatic speed control. This study refines an existing control algorithm, beetle antennae optimization (BAO), by integrating elements of particle swarm optimization (PSO) and a beetle antennae search algorithm (BAS), further enhanced by chaos mapping and an adaptive weighting factor. These modifications aim to improve the algorithm’s search capabilities and mitigate the risks of settling into local optima. Unlike previous iterations, this study includes rigorous dynamic and stability analyses focusing on key performance metrics such as settling time, overshoot, and steady-state error. Comparative Simulink/MATLAB modeling demonstrates that the enhanced BAO algorithm significantly outperforms traditional PID control, BAS, and adaptive weighted-PSO in reducing static error, overshoot, and adjustment time under various conditions, including scenarios with external disturbances. Our results indicate a 60% improvement in the optimization performance of speed curve metrics, confirming the enhanced efficacy and robustness of the robotic control system. This research offers valuable insights into the advantages of the refined BAO algorithm, providing a comprehensive basis for its practical application in industrial robotic control systems.

A Fusion Positioning Method for Indoor Geomagnetic/Light Intensity/Pedestrian Dead Reckoning Based on Dual-Layer Tent-Atom Search Optimization-Back Propagation.

Indoor positioning using smartphones has garnered significant research attention. Geomagnetic and sensor data offer convenient methods for achieving this goal. However, conventional geomagnetic indoor positioning encounters several limitations, including low spatial resolution, poor accuracy, and stability issues. To address these challenges, we propose a fusion positioning approach. This approach integrates geomagnetic data, light intensity measurements, and inertial navigation data, utilizing a hierarchical optimization strategy. We employ a Tent-ASO-BP model that enhances the traditional Back Propagation (BP) algorithm through the integration of chaos mapping and Atom Search Optimization (ASO). In the offline phase, we construct a dual-resolution fingerprint database using Radial Basis Function (RBF) interpolation. This database amalgamates geomagnetic and light intensity data. The fused positioning results are obtained via the first layer of the Tent-ASO-BP model. We add a second Tent-ASO-BP layer and use an improved Pedestrian Dead Reckoning (PDR) method to derive the walking trajectory from smartphone sensors. In PDR, we apply the Biased Kalman Filter-Wavelet Transform (BKF-WT) for optimal heading estimation and set a time threshold to mitigate the effects of false peaks and valleys. The second-layer model combines geomagnetic and light intensity fusion coordinates with PDR coordinates. The experimental results demonstrate that our proposed positioning method not only effectively reduces positioning errors but also improves robustness across different application scenarios.

Coal and Gas Outburst Prediction Model Based on Miceforest Filling and PHHO–KELM

Coal and gas outbursts are some of the most serious coal mine disasters, and effective prediction of coal and gas outbursts can reduce the likelihood of accidents and fatalities. Previously conducted studies have established that machine learning has achieved results in the prediction of coal and gas outbursts, but there is a problem that the available accident data of coal and gas outbursts are diminished or missing. This paper proposes a prediction model based on multiple filling of chained equations for random forests (miceforest) and the Harris Hawk optimization algorithm with Piecewise chaos mapping (PHHO) to optimize the kernel extreme learning machine (KELM) to solve the problem of missing data in coal and gas outburst prediction and to improve prediction accuracy in the case of missing data. Firstly, the miceforest algorithm was adopted to fill missing values in the salient samples, and then the PHHO algorithm was used to optimize the parameters of KELM. Finally, the datasets before and after filling were input into the PHHO–KELM model for experimentation and comparison with other models. The results show that miceforest filling is effective in improving the salient sample accuracy and overall accuracy of predictions, but the improvement is not significant for non-salient samples. The use of the PHHO–KELM model can effectively avoid falling into a local optimum and further improve the prediction accuracy of the KELM algorithm. The salient sample accuracy and overall accuracy of the miceforest–PHHO–KELM model prediction are 96.77% and 98.50%. And an effective coal and gas outburst model has been proposed, which is the miceforest–PHHO–KELM model.

Application of SVR models built with AOA and Chaos mapping for predicting tunnel crown displacement induced by blasting excavation

This study utilizes the support vector regression (SVR) model to predict the tunnel crown displacement (TCD) induced by the blasting excavation. 95 blasting operations considering the tunnel shape parameters, the blasting parameters, the explosive parameters, and the rock mass properties are used to train and test models. To enhance the model prediction performance, a novel combination of the arithmetic optimization algorithm (AOA) with the chaos mapping (CM) called CMAOA, is proposed to select the optimal hyperparameters of the SVR model with advantages of the computing speed and the optimization ability. Sixteen benchmark functions are adopted to verify the authenticity of the prediction performance improvement of the CMAOA, and six statistical indices are used to evaluate the performance of the proposed hybrid models for predicting the TCD. The evaluation results show that the SVR optimized by the AOA with Circle map (CirAOA-SVR) model has the best prediction accuracy among all models. The results of parameter analysis indicate that the degree of bonding of bedding surface is the most important input parameter for predicting the TCD, but the blasting and explosive parameters with fine importance scores cannot be ignored either.