Chaos Feature Research Articles

A Complex Region of Europa's Surface With Hints of Recent Activity Revealed by Juno's Stellar Reference Unit

AbstractOn 29 September 2022 Juno's low‐light sensitive Stellar Reference Unit (SRU) captured a high‐resolution (256–340 m/pixel) broadband (450–1,100 nm) visible image of Europa's icy surface during the first close flyby of the Jovian moon since Galileo's last encounter in 2000. Collected at a sub‐spacecraft altitude of 412 km while the surface was illuminated only by Jupiter‐shine (incidence angle: 48–51°), the SRU image reveals a 3 × 104 km2 region between ∼0°–6°N and 43.5°–51°W in remarkable detail at the highest resolution to date. Prior coverage by Galileo under high‐sun conditions at 1 km resolution led to the characterization of the terrain as ridged plains with undifferentiated linea. The SRU image reveals a much richer and more complex picture. Intricate networks of cross‐cutting ridges and lineated bands surround an intriguing 37 km (east‐west) by 67 km (north‐south) chaos feature with a concentric fracture system, depressed matrix margins, and low‐albedo materials potentially associated with brine infiltration. The morphology and local relief of the chaos feature are consistent with formation in the collapse of ice overlying a salt‐rich lens of subsurface water. Low‐albedo deposits, similar to features previously associated with hypothesized cryovolcanic plume activity, flank nearby ridges. The SRU's high‐resolution view of many types of features in a single image allows us to explore their regional context and greatly improve the geologic mapping of this part of Europa's surface. The image reveals several relatively youthful features in a potentially dynamic region, providing baselines for candidate locations that future missions can investigate for present day surface activity.

Chaotic Sand Cat Swarm Optimization

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This algorithm combines the features of the recently introduced SCSO with the concept of chaos. The basic aim of the proposed algorithm is to integrate the chaos feature of non-recurring locations into SCSO’s core search process to improve global search performance and convergence behavior. Thus, randomness in SCSO can be replaced by a chaotic map due to similar randomness features with better statistical and dynamic properties. In addition to these advantages, low search consistency, local optimum trap, inefficiency search, and low population diversity issues are also provided. In the proposed CSCSO, several chaotic maps are implemented for more efficient behavior in the exploration and exploitation phases. Experiments are conducted on a wide variety of well-known test functions to increase the reliability of the results, as well as real-world problems. In this study, the proposed algorithm was applied to a total of 39 functions and multidisciplinary problems. It found 76.3% better responses compared to a best-developed SCSO variant and other chaotic-based metaheuristics tested. This extensive experiment indicates that the CSCSO algorithm excels in providing acceptable results.

A non-ferrous metal price ensemble prediction system based on innovative combined kernel extreme learning machine and chaos theory

Non-ferrous metal futures, as a significant component of the financial market, are complementary and coordinated with other financial elements, which has been a key area of research in recent years. However, given the apparent volatility and chaotic nature of the non-ferrous metal price sequence, forecasting it remains a difficult challenge. While prior research employed a variety of methodologies to forecast metal prices, they overlooked the critical role of chaos feature analysis and the necessity of error analysis, severely limiting prediction accuracy. This paper designs a novel non-ferrous metal price ensemble prediction system that incorporates data decomposition, phase space reconstruction, multi-objective optimization, point prediction, and interval prediction. A combined kernel extreme learning machine based on the improved multi-objective lion swarm optimization algorithm is developed and theoretically explained to improve prediction accuracy and reliability. Additionally, the appropriate creation of the prediction interval based on the best-fit distribution of the point prediction error enabled the examination of various levels of uncertainty. In an empirical experiment using copper and aluminum prices from the London Metal Exchange, the proposed system demonstrated benefits in point and interval prediction, providing decision makers with useful prediction references.

A Pilot Study of Chaos Criteria with Hilbert Transform and Mutual Information

Chaos feature criteria tree has many fruits with geometric maps and calculated values. To easily understand how to study chaos identification, we propose one new learning combination solution based on Hilbert transform (HT) and mutual information (MI). The most important coding probe (x-axis) used the HT of locked uniformly distributed random number (LUSRN), so far the y-axis in contrast standard map points to LUDRN. The measure between contrast standard map and unknown map (normalized test data, HT of LUSRN) used MI values. The test cases cover five chaos equations and eighteen quasi-period functions. The results show in statistics that our new maps and corresponding MI values can classify three kinds of signals with periodic, chaotic and random states. And this work may promote rapid growth of an apple embedded in novel chaos criteria.

Securing templates in a face recognition system using Error-Correcting Output Code and chaos theory

In biometric cryptosystems, biometric data is combined with cryptography algorithms to generate secure templates. In these systems, creating protected templates with both high discriminability and high security is a challenging issue. To address this issue, this paper proposes a new face cryptosystem based on binarization transformation, chaos feature permutation and fuzzy commitment scheme. To enhance discriminability, real-valued templates are converted into their binary versions using a new discriminant binarization transformation based on Error-Correcting Output Code. Then, the chaos feature permutation is used to increase the security and privacy of binary templates, and also to protect the fuzzy commitment scheme against cross-matching attacks. The proposed scheme is evaluated on three well-known face databases, i.e. CMU PIE, FEI, and Extended Yale B. Experimental results show that the proposed method improves discriminability, as well as privacy and security of the system, compared to the existing face template protection algorithms.

Spontaneous combustion influenced by surface methane drainage and its prediction by rescaled range analysis

This study established numerical modeling using COMSOL™ to examine the influence of horizontal location and drainage ability of surface borehole on spontaneous combustion in longwall working face gob. Rescaled Range Analysis (R/S analysis) was employed to investigate the chaos characteristic of N2/O2 ratio from a surface borehole in 10416 working face gob, Yangliu Colliery, China. The simulation results show that there is always a circular “dissipation zone” around the drainage borehole and an elliptic “spontaneous combustion zone” in deep gob. Little influence was found on spontaneous combustion zone on the intake side of the gob but the width of spontaneous combustion zone in middle gob is enlarged, while the depth of spontaneous combustion zone near the return side is reduced. The R/S analysis indicates that the influence of surface borehole on spontaneous combustion can be divided into two stages by the chaos feature of N2/O2: safety drainage stage and spontaneous combustion initiating stage. It can be concluded that the methane drainage from gob through surface borehole can intervene in the distribution of spontaneous combustion zone in gob and the chaos feature of N2/O2 from surface borehole can effectively reflect coal spontaneous combustion condition in gob.

Time-Series Analysis Technologies Applied to the Study of Carbon Element Distribution Along Casting Direction in Continuous-Casting Billet

The carbon element distributions along casting direction at centerline position, V-shaped segregation band, and columnar to equiaxed transformation (CET) position in continuous-casting billet have been first obtained by the original position statistic distribution analysis (OPA). Different location points at a certain position were formed along the opposite direction of casting direction with the lapse of the time, so the carbon mass fractions of different location points at a certain position can be considered as a time series. Based on the actual result, some typical time-series analysis technologies, such as the autoregressive integrated moving average (ARIMA) model, were used to analyze the characteristics of the three time series. It is found that there is some periodicity in the carbon element distribution at centerline position, and the correlation relationship between centerline position and CET position is closer than that between the centerline position and V-shaped segregation band at the same location point along the casting direction. Moreover, the element distribution along the casting direction is one of the solidifying results with the lapse of the time, so it is possible to investigate the characteristics of the system about the solidification process of continuous-casting billet by using these time series. Then it shows that there is a chaos feature in the system about the solidification process of studied continuous-casting billet on the basis of Hurst exponents and saturated correlative dimensions. Meanwhile, the formation process of carbon element distribution along casting direction can be described as fractional Brownian motion, and the upper and lower limits of the number of internal independent variables were determined in order to describe the formation process of carbon element distributions at different positions of the studied billet. Finally, the stochastic extent at V-shaped segregation band is found to be the largest, and it needs more independent variables to describe its formation process than the other two positions.

Stream Function of Cohesive Mixing in the Tank of a Planetary Mixer

Planetary mixers are indispensable to mixing of some cohesive mixture. The change of stream function is related to the shear velocity of the fluid flow of a mixer, and reveals the chaos feature of the mixing system. In this study, with assumptions of fluid being 2-D incompressible cohesive and mixing dynamic system quasi-static, stream function models of a single blade mixer and the twin blade one were presented. Research results show that: comparing with single blade mixer, the twin blade one decreases the area of zero shear velocity, which is good to the mixing process; there are areas of rapid change of stream function in mixing domain of the twin blade mixer, which is quite good to the mixing process and performance.

Research on Lyapunov Exponent of Chaos Feature in Different Stress Stage of Concrete Joint System

: According to the research on whole curves of strain-stress of concrete materials, the paper illustrates in evidence features of stages and shows that the discrete feature of curve often occurs in strain-softening stage. After chaotic dynamic analysis of testing datas, it presents that system of whole process of strain-stress evolves from ordered steady state to low chaotic state and then to high chaotic state along with increase of compressive strength. The linear relationship of strain- stress grows evident and the system evolves from strong ordered steady state to low chaos state. The strain-stress system before compressive strength peak is basically in weak chaotic state. The is proposed to be the stability criterion of concrete features in different stress stages, and the is regarded as the representative value of the system stability degree. The calculation of example shows that the stability criterion definited by the proposed method is consistent with the actural situation.

Research on Chaos Feature and Forecasting of Air Conditioning Load

Evolution law on air conditioning load is affected by many factors which are very difficult to be known and gained, that which results in low precision of simulation and forecast. Based on analysis on chaos characteristic of air conditioning load time series, BP neural networks model based on chaos phase space is proposed to forecast air conditioning load through embedding dimension. Considering influence of dynamical factor of air conditioning load as well as difficulty of calculating number of input cell, the model is provided with strong nonlinear mapping capacity, is applied to simulate and forecast air conditioning load, the outcomes is reasonable and higher precision.

Detection of ship targets on the sea surface based on Lyapunov exponents of image block

The change of background's chaos feature when there are moving targets was analysed, and a new algorithm based on Lyapunov exponents of image block for moving targets detection on the sea surface, which is used for ship targets detection on the sea surface, was proposed. The new method defines the term of image gray level distance, extends the calculation method of Lyapunov exponents from single dimension signal to image signal based on the improved Wolf method, uses the change of the largest gray level distance Lyapunov exponents to detect ship targetssubmerged in chaos background. Experimental results show that the sea surface background has chaos feature. The proposed algorithm can detect ship targets effectively and can get better result than statistical analysis.

Chaos feature of test particle in the gravitational field with a quadrupole

In this paper we investigate the dynamics of a test particle in the gravitational field with a quadrupole. By constructing Poincare sections for different values of the parameters and initial conditions, we find a chaotic evolution. From these Poincare sections, we further confirm that the chaotic evolution of the test particle originates from the quadrupole.