Proper Parameters Research Articles

Enhancing colour image encryption through parameters optimization of memristive hyperchaotic system with CPSO algorithm and LSAIM

ABSTRACT Particle Swarm Optimization (PSO) is a simple and effective algorithm for optimization. However, it often converges too early and depends heavily on proper parameter settings for good results, especially in complex or high-dimensional search spaces. Therefore, this algorithm is inappropriate for parameter optimization of hyperchaotic systems and may not provide sufficient robustness and security of colour images. This study presents a new colour image encryption based on optimizing a four-dimensional Memristor-based hyperchaotic system, in which the fourteen parameters of the hyperchaotic system are optimized by the Chaotic Particle Swarm Optimization (CPSO) method, in combination with DNA code and a new logistic sine adjusted integrated map (LSAIM). Simulation results have shown that the algorithm achieved a key space of up to 21116, providing sufficient protection against brute-force attacks, with entropy values close to the ideal (7.9994), NPCR of 99.6178%, and UACI of 33.965%, indicating robust security.

The impact of parameter variation in the quantification of forensic genetic evidence

Technological advancements have allowed the detection of increasingly complex forensic genetics samples, as minimum amounts of DNA can now be detected in crime scenes or other settings of interest. The weight of the evidence depends on several parameters regarding the population and sample-related analytical factors, the latter in a greater number when the DNA amount is considered. This led to the development of probabilistic genotyping software (PGS), able to deal with the associated complexities. This study aims to evaluate the impact on the evidence’s weighing, when different analytical threshold values are used, and when different models and/or estimates for analytical artifacts, such as stutters or drop-in parameters, are considered. To reach this goal, three PGS, based on different statistical models, were used to analyze real casework pairs of samples composed of a mixture with either two or three estimated contributors, and a single-source sample associated. The obtained results show that the estimation of these parameters must not be overlooked, as they may considerably impact the outcome. This underlines the importance of proper parametrization in the analysis of forensic genetics identification problems when using complex samples, and the understanding by practitioners of how probabilistic genotyping informatics tools work to use them accurately.

Parameter Design in Production by Means of Robust Fuzzed PMOO in Case of Desirable Target

A proper parameter design in the production process is critical to guarantee the quality of products and their improvement. The rationality of the previous traditional approaches for robust assessment including the Taguchi method and dual response method is questionable. In this article, the combination of probabilistic multi-objective optimization (PMOO) with membership approach in fuzzy theory is developed to conduct parameter design of production in case of desirable target with robustness deeply, which is furthermore applied to two examples of both parametric design of gas metal arc (GMA) welding process and printing machine's ability. In the new approach, the mean value of "complement" of membership value of a set of test data belonging to its desired target of an objective response is taken as one sub-objective response, which is an unbeneficial type of index in the assessment to contribute the first part of the partial preferable probability of the objective. In contrast, the dispersion of a set of test data in terms of membership with respect to the desired target value is taken as the other sub-objective response to contribute the second part of the partial preferable probability of the objective simultaneously, which is an unbeneficial index. Thus, the fuzzed PMOO approach is regulated comprehensively. Besides, the consequences of application examples reflect the reasonability of the approach as an auxiliary measure for PMOO consistently to perform optimal robust design.

Chaos Synchronization in a Dual-Laser Chaotic Multiplexing System Based on Nanolasers

Nanolaser (NL), as an important optical source device, has a significant impact on photonic integrated circuits and has become a research hotspot in recent years. This study investigates the synchronization performance of a dual-channel laser chaotic multiplexing system based on NLs and employs an active-passive decomposition to enhance signal processing and multiplexing efficiency. By establishing a rate equation model, the synchronization characteristics of the system were analyzed, focusing on the effects of two key parameters—the Purcell factor (F) and the spontaneous emission coupling factor (β)—as well as system parameters, single-parameter mismatches, and multi-parameter mismatches. Numerical simulations show that, with proper parameter configurations, the two master NLs can maintain low correlation, ensuring the "pseudo-orthogonal" of chaotic signals while achieving high-quality chaotic synchronization with their paired slave NLs. The study found that both the Purcell factor (F) and the spontaneous emission coupling factor (β) significantly influence the synchronization performance of the system, and the optimal parameter ranges for achieving high-quality synchronization were identified. Additionally, the effects of feedback strength and frequency detuning were explored, revealing that frequency detuning plays a more critical role in the synchronization between the master NLs. The impact of parameter mismatches on system synchronization performance was also emphasized. The system exhibits robustness against single-parameter mismatches, with minimal impact on master-slave synchronization quality. However, multi-parameter mismatches introduce more complex effects. Compared to traditional semiconductor laser systems, this system can maintain "pseudo-orthogonal" over a wider parameter range, achieving higher security and lower channel interference. This research lays a theoretical foundation for chaos synchronization based on NLs and provides new insights for designing secure, stable, and efficient optical communication systems.

A Genetic Algorithm Based ESC Model to Handle the Unknown Initial Conditions of State of Charge for Lithium Ion Battery Cell

Classic enhanced self-correcting battery equivalent models require proper model parameters and initial conditions such as the initial state of charge for its unbiased functioning. Obtaining parameters is often conducted by optimization using evolutionary algorithms. Obtaining the initial state of charge is often conducted by measurements, which can be burdensome in practice. Incorrect initial conditions can introduce bias, leading to long-term drift and inaccurate state of charge readings. To address this, we propose two simple and efficient equivalent model frameworks that are optimized by a genetic algorithm and are able to determine the initial conditions autonomously. The first framework applies the feedback loop mechanism that gradually with time corrects the externally given initial condition that is originally a biased arbitrary value within a certain domain. The second framework applies the genetic algorithm to search for an unbiased estimate of the initial condition. Long-term experiments have demonstrated that these frameworks do not deviate from controlled benchmarks with known initial conditions. Additionally, our experiments have shown that all implemented models significantly outperformed the well-known ampere-hour coulomb counter integration method, which is prone to drift over time and the extended Kalman filter, that acted with bias.

Morphological changes in cerebrospinal fluid production.

The ventricular system and subarachnoid space are filled with cerebrospinal fluid, which plays a key role in the nervous system. This fluid is produced by the choroid plexus, an organ rich in ion transporters that precisely control the transport of specific ions into the cerebrospinal fluid thanks to tight junctions between the plexus cells; these prevent the passage of substances other than the transporters, thus allowing for precise control of the fluid composition. Cerebrospinal fluid production is based on a network of interrelationships between specific ion flows enabled by the numerous transporters. The fluid is cleaned and resorbed by the glymphatic system via multiple absorption pathways. Maintaining proper cerebrospinal fluid parameters is extremely important for proper brain function. Considering the fragility of the brain, even small fluctuations in cerebrospinal fluid composition can impair its condition. Therefore, to understand the nervous system, it is important to have thorough knowledge of the production, transport, and resorption mechanisms of cerebrospinal fluid. The aim of this paper is to summarize the current state of knowledge about the mechanisms of production, pathways of absorption and physiological values of cerebrospinal fluid parameters; it also discusses the role of the glymphatic system in maintaining fluid homeostasis, and the changes resulting from its dysfunction as result of trauma.

Effects of temperature and duration on extraction process utilising aqueous solvent towards quality of tea derived from dried roots of Asparagus officinalis L.

Introduction: The root of Asparagus officinalis L. stands out as a potential byproduct source for creating value-added food products. Asparagus root is studied for the development of a new herbal tea. The water solvent extraction method is proposed to determine appropriate extraction parameters, including temperature and time, which are crucial and interdependent factors during the extraction process, requiring precise investigation to maximise yield and maintain the quality of the extracted compounds. Methods: This investigation focused on identifying an advanced extraction method applicable to the tea infusion of desiccated herbal products. The study delved into multiple extraction parameters, encompassing temperatures (80, 85, 90, and 95°C) and durations (15, 30, 45, and 60 minutes), utilising an aqueous solvent to scrutinise their impact on the tea’s extracted solution quality. Results: The research determined that 85°C and 30 minutes were the proper parameters for tea infusion. The extracted solution from dried asparagus root tea exhibited ultimate nutrient contents (saccharose, vitamin C, phenolic compounds, flavonoid, and saponin) and antioxidant capacity (DPPH and FRAP) (in 100 g of dry matter) of 0.81 g, 1.23 g, 0.50 g TAE, 0.12 g QE, 1.10 g SE, 56.10%, and 1.12 M Fe2+, respectively. Conclusion: This research effectively elucidated the impacts of extraction temperature and duration employing an aqueous solvent on desiccated agricultural commodity, particularly asparagus root. The findings yielded valuable insights aimed at discerning optimal parameters for the extraction procedure based on nutrient content and bioactivity in the extracted solution.

ANALYSIS OF FRICTION STIR WELDING JOINTS ON HIGH DENSITY POLYETHYLENE (HDPE) DUE TO CHANGE IN MATERIAL TEMPERATURE AND SPINDLE ROTATION

Friction stir welding (FSW) can be a solution in solving the problem of joining thermoplastic polymers which are difficult to do with fusion welding. Selection of the proper FSW connection parameters can produce optimal connection quality in HDPE material joints. In friction stir welding, the heat of welding is used to raise the temperature of the material from its initial state to the solid state temperature. If the material temperature (T0) is increased above room temperature (TRoom), while the FSW welding parameter is constant, then at a certain increase in T0 it will produce excess welding heat which results in excessive softening or even melting of the material being welded. Therefore, the material temperature setting (T0) becomes an important parameter FSW The process of connecting the HDPE plates with friction stir welding was carried out by varying the material temperature (T0 ) of TRoom, 50 0C, 75 0C and 100 0C. While the spindle rotation varies 2000 rpm and 2500 rpm. Other parameters controlled include the type of square butt joint, 5 mm HDPE plate thickness, 20 mm/min feed rate, 0.1 mm plunge depth, 20 mm tool shoulder diameter, 4.5 mm pin length and 6 mm pin diameter. The result is that the higher the spindle rotation and the material temperature during the welding process causes the FSW peak welding temperature to increase. The highest peak weld temperature was obtained at variations in material temperature (T0) 100 °C with a spindle rotation of 2500 rpm, which is 123.2 °C or 94% of the melting temperature of HDPE. The hardness of the weld nugged area is still lower than the hardness of the base material. The highest average tensile strength of the research variations was achieved at a spindle rotation speed of 2500 rpm and a material temperature of 75 °C of 16.8 MPa, higher than the average tensile strength of the base material which is 15.1 MPa.

Improved Surface Quality and Microstructure Regulation in High Power Fiber Laser Cutting of Stainless Steel Grid Plates.

In order to disintegrate nuclear fuel rods in the grid connection structure, a 10 kW fiber laser was used to cut a stainless steel simulation component with four layers of 3 mm thick plates and 12 mm gaps. The slit width is regarded as an important indicator to evaluate the cutting quality of the four-layer stainless steel plate. The results showed that good laser cutting quality can be successfully achieved under the proper process parameters. The widths of the cut seams of the four layers of grating after cutting were 1.25, 1.65, 1.80, and 1.92 mm. As the auxiliary gas pressure decreased layer by layer, the metal melting pool for the first two plates was mainly destroyed by the auxiliary gas. The cutting quality was good, and the slit area was mainly austenite with the presence of some ferrite. The third- and fourth-layer plates almost had no gas flow to assist blowing off, so the cut surface was an uneven melting pit, the cutting quality was poor, and the cut seam area ferrite content was higher than the upper plate cut seam area. At the same time, due to the lack of airflow cooling of the bottom plate, high laser energy, and long heating time, grain coarsening occurred, while grain deformation and a large number of dislocations existed. It can provide process support and technical guidance for the disintegration of nuclear fuel rods.

Leveraging Movement Representation from Contrastive Learning for Asteroid Detection

To support asteroid-related studies, current motion detectors are utilized to select moving object candidates based on their visualizations and movements in sequences of sky exposures. However, the existing detectors encounter the manual parameter settings which require experts to assign proper parameters. Moreover, although the deep learning approach could automate the detection process, these approaches still require synthetic images and hand-engineered features to improve their performance. In this work, we propose an end-to-end deep learning model consisting of two branches. The first branch is trained with contrastive learning to extract a contrastive feature from sequences of sky exposures. This learning method encourages the model to capture a lower-dimensional representation, ensuring that sequences with moving sources (i.e., potential asteroids) are distinct from those without moving sources. The second branch is designed to learn additional features from the sky exposure sequences, which are then concatenated into the movement features before being processed by subsequent layers for the detection of asteroid candidates. We evaluate our model on sufficiently long-duration sequences and perform a comparative study with detection software. Additionally, we demonstrate the use of our model to suggest potential asteroids using photometry filtering. The proposed model outperforms the baseline model for asteroid streak detection by +7.70% of f1-score. Moreover, our study shows promising performance for long-duration sequences and improvement after adding the contrastive feature. Additionally, we demonstrate the uses of our model with the filtering to detect potential asteroids in wide-field detection using the long-duration sequences. Our model could complement the software as it suggests additional asteroids to its detection result.

Machine learning assisted design and preparation of Fe85Si2B8.5P3.5C1 amorphous/nanocrystalline alloy with high Bs and low Hc

Four machine learning (ML) models including eXtreme Gradient boosting (XGBT), k-Nearest Neighbor (kNN), Gradient Boosting Decision Tree (GBDT) and Artificial Neural Network (ANN) were employed to predict saturation flux density (Bs), coercivity (Hc), grain size, magnetostriction (λ), and Curie temperature (Tc) of Fe-based amorphous/nanocrystalline alloys. To maximize predictive ability of ML models, grid-search and normalization were used to search the most proper parameters of ML and pre-process raw data, respectively. XGBT had best predictive and generalization ability for predicting Bs and Hc with coefficient of determination (R2) of 0.992 and 0.967, respectively. Based on the feature importance analysis from the XGBT model, the Fe85Si2B8.5P3.5C1 amorphous alloy ribbon with good magnetic properties, such as high Bs, low Hc, was designed and prepared by melt spinning. X-ray diffraction (XRD), differential scanning calorimetry (DSC), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), B–H loop tracer, and magnetostriction instrument were used to identify the phase structure and physical properties of the Fe85Si2B8.5P3.5C1 alloy. It was found that the Fe85Si2B8.5P3.5C1 alloy had good magnetic properties with Bs of 1.82 T and the Hc of 2.02 A/m after annealing at 723 K for 180 s, in good agreement with the designed results by machine learning.

Fractals as Pre-Training Datasets for Anomaly Detection and Localization

Anomaly detection is crucial in large-scale industrial manufacturing as it helps to detect and localize defective parts. Pre-training feature extractors on large-scale datasets is a popular approach for this task. Stringent data security, privacy regulations, high costs, and long acquisition time hinder the development of large-scale datasets for training and benchmarking. Despite recent work focusing primarily on the development of new anomaly detection methods based on such extractors, not much attention has been paid to the importance of the data used for pre-training. This study compares representative models pre-trained with fractal images against those pre-trained with ImageNet, without subsequent task-specific fine-tuning. We evaluated the performance of eleven state-of-the-art methods on MVTecAD, MVTec LOCO AD, and VisA, well-known benchmark datasets inspired by real-world industrial inspection scenarios. Further, we propose a novel method to create a dataset by combining the dynamically generated fractal images creating a “Multi-Formula” dataset. Even though pre-training with ImageNet leads to better results, fractals can achieve close performance to ImageNet under proper parametrization. This opens up the possibility for a new research direction where feature extractors could be trained on synthetically generated abstract datasets mitigating the ever-increasing demand for data in machine learning while circumventing privacy and security concerns.

Science Outcome Design Parameters for Cluster-Randomized Trials Involving Teachers

Programs aiming to improve student science outcomes often involve teachers. When designing cluster-randomized trials (CRTs) to evaluate the causal effects of these programs, investigators need empirical design parameters as critical inputs in the study design phase. Using proper empirical design parameters to conduct power analysis, investigators can ensure designs have adequate statistical power to detect treatment effects and efficiently use resources. The current study uses two nationally representative samples to estimate empirical design parameters for science outcomes in settings of students (nested within teachers) nested within schools. We compile design parameters for intraclass correlation coefficients and proportions of variance explained by covariates. We illustrate how to use the results to design efficient and effective CRTs in both the conventional statistical power analysis framework and the optimal design framework that additionally considers sampling costs.

OWA operators in the insurance industry

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

Structural dynamic characteristics of vertical axis wind and tidal current turbines

Vertical axis turbines have received great attention in both offshore wind and tidal current energy communities considering their advantages of economic design and unidirectional operation. However, their commercialization process is rather slow compared with the development of horizontal axis turbines due to technical challenges resulting from higher loads from unsteady aero/hydrodynamic forces, centrifugal forces and gravity of the structures. These are mainly because, while the inherent unsteady tangential pulls and fatigue loads intensify the structural vibration, aggravating structural safety and fatigue life, the relationship between the geometric parameters and the structural responses of blades remains unclear. Therefore, in order to clarify this issue, in this study, we focus on the modal and transient analysis of vertical axis turbines using multi-body dynamics, geometrically exact beam theory and detached eddy simulation. We find that the natural frequency of the rotor is directly related to the ratio of blade length and arm length. The effect of arms cannot be ignored in the structural analysis of the turbine. Moreover, an increase of blade length intensifies the deformation and vibration of the turbine’s structure, making the turbine more prone to fatigue failure. This article is expected to extend the existing knowledge of wind and tidal current turbines and provide a reference for choosing proper design parameters and developing suitable-capacity vertical axis turbines.

Regular reflection to Mach reflection (RR–MR) transition in short wedges

Regular reflection (RR) to Mach reflection (MR) transitions ( ${\rm RR}\leftrightarrow {\rm MR}$ ) on long wedges in steady supersonic flows have been well studied and documented. However, in a short wedge where the wedge length is small, the transition prediction becomes really challenging owing to the interaction of the expansion fan emanating from the trailing edge of the wedge with the incident shock and the triple/reflection point. The extent of this interaction depends on the distance between the wedge trailing edge and the symmetry line (Ht). This distance is a geometric combination of the distance of the wedge leading edge from the symmetry line $(H)$ , the wedge angle ( $\theta$ ) and the wedge length $(w)$ . In the present study, we used the method of characteristics to model the complete wave interactions which accurately predicted shock curvatures and the reflection configurations for all ranges of the incoming flow Mach number. In the case of short wedges, the transition criterion strongly depends on the wedge length, which can be so adjusted even to eliminate the ${\rm RR}\rightarrow {\rm MR}$ transitions till the wedge angle reaches the no-reflection domain. Transition lines for both the detachment criterion and von Neumann criterion are also drawn to investigate the dual solution domain, and the reflection configurations were verified experimentally for the first time on short wedges. By using proper input configuration parameter ( $w/H$ ), various types of shifts in the dual solution domain for short wedges are studied and categorised into three types, namely Type I, Type II and Type III.

A non-linear integer-valued autoregressive model with zero-inflated data series

A new non-linear stationary process for time series of counts is introduced. The process is composed of the survival and innovation component. The survival component is based on the generalized zero-modified geometric thinning operator, where the innovation process figures in the survival component as well. A few probability distributions for the innovation process have been discussed, in order to adjust the model for observed series with the excess number of zeros. The conditional maximum likelihood and the conditional least squares methods are investigated for the estimation of the model parameters. The practical aspect of the model is presented on some real-life data sets, where we observe data with inflation as well as deflation of zeroes so we can notice how the model can be adjusted with the proper parameter selection.

Dynamical effects of low-frequency and high-frequency current stimuli in a memristive Morris–Lecar neuron model

This paper deduces that the potassium and calcium ion currents in the two-dimensional (2D) Morris–Lecar neuron model can be respectively characterized by a passive memristor and a locally active memristor. Then a memristive Morris–Lecar neuron model with an equivalent circuit scheme is first constructed. The equilibrium trajectory and its stability are analyzed, which displays fold and Hopf bifurcations with proper model parameters. Bursting behavior and mixed-mode oscillations with after-depolarizing potential for low-frequency current stimulus are numerically disclosed. Besides, the bifurcation mechanisms for bursting behavior and mixed-mode oscillations are theoretically deduced. Furthermore, the firing patterns and antimonotonicity phenomenon for high-frequency current stimulus are numerically discovered. This study provides a new concept for building memristive bionic circuits from neuron models with well-defined ion channel currents.

Autonomous intelligent additive manufacturing of continuous fiber-reinforced composites: data-enhanced knowledgebase and multi-sensor fusion

ABSTRACT Additive manufacturing (AM) are an emerging technique to generate complex structures of composite. However, the instability of the AM process leads to adverse manufacturing effects, including dimensional inaccuracies and poor mechanical performance in CFRP composites. Online monitoring and adaptive control based on autonomous cognition are suggested to ensure the accuracy of as-fabricated parts and enhance their quality upon manufacturing. In this study, a method of online monitoring and self-adaptive control based on multi-sensor fusion is proposed to predict and adjust the process parameters during AM of CFRP. The force sensor, visual camera, and thermal camera are employed to obtain the multiple signals upon manufacturing and then realize the autonomous perception, cognition, and decision features. Herein, the quantitative correlations between local defects and multi-sensing features are established to guide the decision-making of closed-loop adjustment. Besides, an empirical surrogate model between the misalignment of fiber bundles and input parameters is built to predict the proper parameters. Moreover, the temperature difference and contact force are selected as the controlled features, which are acquired using a thermal camera and a force sensor. The proposed system offers a novel framework for bolstering both the stability of the AM process and the quality of fabricated components.

A novel synchrosqueezing transform associated with linear canonical transform

Synchrosqueezing transforms have aroused great attention for its ability in time–frequency energy rearranging and signal reconstruction, which are post-processing techniques of the time–frequency distribution. However, the time–frequency distributions, such as short-time Fourier transform and short-time fractional Fourier transform, cannot change the shape of the time–frequency distribution. The linear canonical transform (LCT) can simultaneously rotate and scale the time–frequency distribution, which enlarges the distance between different signal components with proper parameters. In this study, a convolution-type short-time LCT is proposed to present the time–frequency distribution of a signal, from which the signal reconstruction formula is given. Its resolutions in time and LCT domains are demonstrated, which helps to select suitable window functions. A fast implementation algorithm for the short-time LCT is provided. Further, the synchrosqueezing LCT (SLCT) transform is designed by performing synchrosqueezing technique on the short-time LCT. The SLCT inherits many properties of the LCT, and the signal reconstruction formula is obtained from the SLCT. Adaptive selections of the parameter matrix of LCT and the length of the window function are introduced, thereby enabling proper compress direction and resolution of the signal. Finally, numerical experiments are presented to verify the efficiency of the SLCT.