Two-dimensional Map Research Articles

Evaluation of Polyethylene Glycol (PEG-6000) Induced Drought Stress Tolerant Mungbean Genotypes by using Correlation, Principal Component, Hierarchical Clustering and Multi-Trait Genotype-Ideotypes Distance Index Analysis

Determining a plant's ultimate population under water stress requires seedling establishment and germination. The current study was aimed at identifying mungbean genotypes that were tolerant to drought stress. The treatments were: Factor A: polyethylene glycol 6000 (PEG-6000) inducing different levels of water potential (i.e., 0.0 (distilled water as a control), -0.7, -1, -2, and -4 bar) and Factor B: Thirty-three mungbean genotypes, collected from various national and international organizations. Each genotype's seeds were planted in a petri dish (9 cm diameter) containing sand bed and were moistened with appropriate amounts of water potential and left to develop into seedlings of all genotypes for up to 10 days. The findings showed that when PEG-6000 concentration was raised, germination, shoot and root length, and the associated fresh and dry weights decreased significantly. Among these tested genotypes, BMX-08010-2, BMX-08009-7, BMX-01015, BARI Mung-8, BARI Mung-2, and BU Mung-2 were found to be tolerant against drought stress based on their % germination and germination indices, as well as their seedling traits. Principal component analysis, multi-trait genotype-ideotype distance index (MGIDI), two-dimensional heat map, and hierarchical clustering analysis also revealed the same genotypes to be stable and capable of withstanding water deficiency stress. In response to water stress, the genotypes BARI Mung-1, BARI Mung-3, BU Mung-4, and BMX-05001 showed poor germination indices along with relatively poor seedling characteristics. The results of the present research provide criteria for the selection of drought-tolerant mungbean genotypes in future breeding programs.

Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model

The rotating parts of large and complex equipment are key components that ensure the normal operation of the equipment. Accurate fault diagnosis is crucial for the safe operation of these systems. To simultaneously extract both local and global valuable fault feature information from key components of complex equipment, this study proposes a fault diagnosis network model, named MultiDilatedFormer, which is based on the fusion of transformer and multi-head dilated convolution. The newly designed multi-head dilated convolution module is sequentially integrated into the transformer-encoder architecture, constructing a feature extraction module where the complementary advantages of both components enhance overall performance. Firstly, the sample is expanded into a two-dimensional feature map and then input into the newly designed feature extraction module. Finally, the diagnostic output is performed by the designed patch feature fusion module and classifier module. Additionally, interpretability research is conducted on the proposed model, aiming to understand the decision-making mechanism of the model through visual analysis of the entire decision process. The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. The highest accuracy reached 97.95%, which was up to 10.97% higher than other models. Furthermore, the feasibility of the model is also verified in the actual dataset of the rotating parts of the injection molding machine. The excellent performance of the model on different datasets demonstrates its effectiveness in extracting comprehensive fault feature information and also proves its great potential in practical industrial applications.

A Novel Color Image Encryption Algorithm Based on Hybrid Two-Dimensional Hyperchaos and Genetic Recombination

The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small chaotic range, unstable chaotic state, and lengthy encryption duration. This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. This study introduces two techniques, genetic recombination and clock diffusion, to simultaneously disperse and mix images at the bit level. This study utilizes chaotic sequences in genetic recombination and clock drift to propose an image encryption technique. The data indicates that the method demonstrates high encryption efficiency. At the same time, the key also successfully passed the NIST randomness test, verifying its sensitivity and randomness. The algorithm’s dependability has been demonstrated and can be utilized for color image encryption.

Bearing fault diagnosis by sparse frequency spiral spectrum driven NAF-LDM under strong noise and small samples

Abstract The complexity of background noise and the scarcity of real fault samples seriously affect the diagnostic accuracy of the model. To address this, a noise-robust two-dimensional feature map, the sparse frequency spiral spectrum (SFSM), based on sparse representation theory, is proposed. A bridge penalty coefficient is applied to the sparse representation model to accurately select impact components, and the fast iterative shrinkage threshold algorithm is used to solve for sparse representation coefficients. Sparse reconstructed signals are obtained by convolving the impact patterns with these coefficients, leading to a sparse reconstruction algorithm with reduced computational complexity. Furthermore, the novel non-linear activation-free blocks (NAF Blocks) are embedded into the latent diffusion model to augment small samples, significantly improving image generation speed and quality. The integration of the Swin transformer for feature extraction and classification further enhances diagnostic performance. The superiority of this method is validated on the XJTU-SY dataset, a bearing experimental platform dataset, and enterprise engineering dataset. Experimental results demonstrate that the structural and generalization advantages of NAF Blocks are crucial for improving image quality and inference speed. The noise suppression capability of the proposed method, facilitated by the SFSM feature processing technique, is confirmed through ablation and noise robustness tests. Finally, the Swin transformer’s excellent feature extraction and classification capabilities for SFSM are verified. The proposed method achieves diagnostic accuracies of 99.10% and 98.7% on the XJTU-SY and experimental platform datasets, respectively.

Improved A-STAR Algorithm for Power Line Inspection UAV Path Planning

The operational areas for unmanned aerial vehicles (UAVs) used in power line inspection are highly complex; thus, the best path planning under known obstacles is of significant research value for UAVs. This paper establishes a three-dimensional spatial environment based on the gridding and filling of two-dimensional maps, simulates a variety of obstacles, and proposes a new optimization algorithm based on the A-STAR algorithm, considering the unique dynamics and control characteristics of quadcopter UAVs. By utilizing a novel heuristic evaluation function and uniformly applied quadratic B-spline curve smoothing, the planned path is optimized to better suit UAV inspection scenarios. Compared to the traditional A-STAR algorithm, this method offers improved real-time performance and global optimal solution-solving capabilities and is capable of planning safer and more realistic flight paths based on the operational characteristics of quadcopter UAVs in mountainous environments for power line inspection.

Ultra-wide-angle peripheral refraction using a laser-scanning instrument

We compared the peripheral refractive measurements of a recently proposed laser-scanning instrument with an established peripheral refractor. Two-dimensional refractive maps were obtained using both instruments for 18 young subjects with differing values of central refraction. The comparison shows a strong correlation between devices in the overlapping measurement area, with the new device extending the range of the explored retinal area to a 100-degree-diameter circular patch, compared to the 60°x35° rectangular area of the older peripheral refractor. Larger refractive maps exhibit trends that cannot be easily predicted from narrower scans. These results demonstrate that the new instrument can be a useful tool for assessing wide-angle peripheral optical data in the human eye.

The implementation of algebraic complex lookup tables over non-chain galois ring extensions and henon map in multimedia security

Abstract Image encryption is crucial for web-based data storage and transmission. Complex algebraic structures play a vital role in providing unique features and binary operations. However, current algebraic-based techniques face challenges due to limited key space. To tackle this issue, our study uniquely connects the algebraic structures with a chaotic map. The study introduces a complex non-chain Galois ring structure and a 12-bit substitution box for image substitution. An affine map is utilized to permute image pixels, and the 12-bit substitution box is uniquely mapped to a Galois field for encryption. A two-dimensional Henon map is employed to generate different keys for the XOR operation, resulting in an encrypted image. The resilience of the scheme against various attacks is evaluated using statistical, differential, and quality measures, showcasing its effectiveness against well-known attacks.

New optical soliton solutions and dynamic behaviours analysis of the Sasa-Satsuma equation in optical fibers

This article investigates novel exact solutions of the Sasa-Satsuma equation for describing the nonlinear characteristics of pulse transmission in optical fiber communication. Various new exact solutions of the Sasa-Satsuma equation were derived employing both the ( G ′ G ′ + G + A ) expansion method and the Bernoulli ( G ′ G ) expansion method. To provide a more intuitive depiction of the optical solitons’ behaviour during transmission, we generated two-dimensional graphs, three-dimensional graphs, and contour maps corresponding to the solutions. These images enable us to showcase the dynamic behaviour of this theoretical model and delve into the specific influence of the self-steepening effect on the transmission characteristics of optical solitons described by the new exact solutions to the Sasa-Satsuma equation.

Semantic fMRI neurofeedback of emotions: from basic principles to clinical applications.

During fMRI neurofeedback participants learn to self-regulate activity in relevant brain areas and networks based on ongoing feedback extracted from measured responses in those regions. This closed-loop approach has been successfully applied to reduce symptoms in mood disorders such as depression by showing participants a thermometer-like display indicating the strength of activity in emotion-related brain areas. The hitherto employed conventional neurofeedback is, however, 'blind' with respect to emotional content, i.e. patients instructed to engage in a specific positive emotion could drive the neurofeedback signal by engaging in a different (positive or negative) emotion. In this future perspective, we present a new form of neurofeedback that displays semantic information of emotions to the participant. Semantic information is extracted online using real-time representational similarity analysis of emotion-specific activity patterns. The extracted semantic information can be provided to participants in a two-dimensional semantic map depicting the current mental state as a point reflecting its distance to pre-measured emotional mental states (e.g. 'happy', 'content', 'sad', 'angry'). This new approach provides transparent feedback during self-regulation training, and it has the potential to enable more specific training effects for future therapeutic applications such as clinical interventions in mood disorders.This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

A novel prediction method for rolling contact fatigue damage of the pearlite rail materials based on shakedown limits and rough set theory with cloud model

Evaluation and prediction of wheel-rail rolling contact fatigue (RCF) damage can provide important theoretical guarantees for the service safety of wheels and rails and help make maintenance easier to plan. This study aims to develop a novel method for evaluating and predicting RCF damage of the pearlite rail materials with various initial shear yield strengths (ke). Based on the rough set mathematical theory incorporated within the cloud model of the comprehensive evaluation index (P0/ke*μt), a novel evaluation and prediction method for RCF damage states of various pearlite rail materials was constructed using the shakedown limits for pearlite rail materials with various initial shear yield strengths. To develop this novel prediction method, different evaluation indices for RCF damage states were designed. A comprehensive certainty approach was introduced to quantitatively analyze the actual measured values of distinct evaluation indices that corresponds to different RCF damage states, wherein the maximum value rule was applied. Moreover, the prediction results were confirmed after further verifying using the actual measured value of the P0/ke*μt. The results indicated that the predicted results were consistent with the test outcomes. The key feature of this prediction method was that it involved both the intrinsic shear yield strength of evaluated pearlite rail materials and wheel-rail rolling contact variables. On the basis of the two-dimensional classical shakedown map, a three-dimensional shakedown limit diagram for rail materials with varying initial shear yield strengths was further constructed using this novel prediction method. The three-dimensional shakedown limit diagram featured an inclined curved surface. As the initial shear yield strength of the pearlite rail materials increased, the curved surface tilted downward, indicating that an increase in the initial ke value of the pearlite rail materials could result in a lower shakedown limit.

Separation and Characterization of Wenjin Tongluo San Essential Oil with a Comprehensive Chromatographic Separation

The essential oil components of traditional Chinese medicine in-hospital preparation were complex, and one-dimensional chromatographic separation was difficult to completely separate them due to the limited peak capacity. This study was carried out to establish a comprehensive two-dimensional chromatographic separation and analysis method based on countercurrent chromatography (CCC) and gas chromatography (GC). In this paper, we focused on the separation of the essential oil of the traditional Chinese medicine in-hospital preparation Wenjing Tongluo San by CCC × GC, and explored the orthogonality between the two chromatographic techniques to provide the new technical support for the screening of the active ingredients. A solvent system composed of n-hexane-ethyl acetate-methanol-water (9.5:0.5:8.5:1.5, v/v) was chosen for the first-dimensional CCC separation. All the fractions collected from CCC were transferred to GC for plotting two-dimensional contours map. The calculated capacity of the two-dimensional separation system exceeded 3000, which was 8 times more than that of the one-dimensional separation system. High orthogonality (r = 0.42) and spatial coverage factor (70.42%) were obtained. Meanwhile, all the fractions were identified by GC-MS. Our research provided a new methodology for separating essential oils in traditional Chinese medicine as well as an approach for evaluating the quality of traditional Chinese medicinal in-hospital preparation based on two-dimensional chromatographic fingerprints.

Gas liquid flow pattern prediction in horizontal and slightly inclined pipes: From mechanistic modelling to machine learning

This paper investigates the prediction of two-phase gas-liquid flow regimes in both horizontal and slightly inclined pipes. For this purpose, the mechanistic model of Taitel et al. (1976) and the machine learning approach have been adopted. First, the mechanistic model was implemented, tested and optimised by introducing factors in the transition equations to determine the configuration that gives the highest prediction accuracy for a specific two-phase system for which experimental data points are available. Second, several machine learning models are trained, tested and additionally validated. This is done by splitting the experimental data set corresponding to the pipe inclination range (−10∘ to 10∘) into training, test and validation sets. The best classifier achieved an accuracy of 95.5% after the test step and up to 98.9% after the validation step. Finally, the Taitel et al. model with the optimal configuration and the best machine learning classifier (XGB classifier) are used to generate the two-dimensional flow regime map.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

An image encryption algorithm based on a novel two-dimensional hyperchaotic map and difference algorithm

An image encryption algorithm based on a novel two-dimensional hyperchaotic map and difference algorithm

Unsupervised machine learning model for detecting anomalous volumetric modulated arc therapy plans for lung cancer patients.

Volumetric modulated arc therapy (VMAT) is a new treatment modality in modern radiotherapy. To ensure the quality of the radiotherapy plan, a physics plan review is routinely conducted by senior clinicians; however, this process is less efficient and less accurate. In this study, a multi-task AutoEncoder (AE) is proposed to automate anomaly detection of VMAT plans for lung cancer patients. The feature maps are first extracted from a VMAT plan. Then, a multi-task AE is trained based on the input of a feature map, and its output is the two targets (beam aperture and prescribed dose). Based on the distribution of reconstruction errors on the training set, a detection threshold value is obtained. For a testing sample, its reconstruction error is calculated using the AE model and compared with the threshold value to determine its classes (anomaly or regular). The proposed multi-task AE model is compared to the other existing AE models, including Vanilla AE, Contractive AE, and Variational AE. The area under the receiver operating characteristic curve (AUC) and the other statistics are used to evaluate the performance of these models. Among the four tested AE models, the proposed multi-task AE model achieves the highest values in AUC (0.964), accuracy (0.821), precision (0.471), and F1 score (0.632), and the lowest value in FPR (0.206). The proposed multi-task AE model using two-dimensional (2D) feature maps can effectively detect anomalies in radiotherapy plans for lung cancer patients. Compared to the other existing AE models, the multi-task AE is more accurate and efficient. The proposed model provides a feasible way to carry out automated anomaly detection of VMAT plans in radiotherapy.

Research on burnup depth measurement of spent fuel board based on thermal neutron imaging technology

The detection equipment for measuring the burnup depth in spent fuel rods is a vital component of the spent fuel management system. which can determine the burnup depth of spent fuel board and plays a crucial role in safeguarding the safety, economic efficiency, and structural integrity of the fuel assembly. This study introduces an innovative technical approach for assessing the burnup depth of spent fuel veneers, utilizing transmitted thermal neutron imaging technology. We have significantly enhanced the design of a thermal neutron moderator collimator, leading to remarkable improvements in the quality of the thermal neutron beam. Following moderation by the collimator, the ultimate thermal neutron injection rate at the designated sample location exceeds 103 n/cm2, with thermal neutrons comprising over 74% of the collimated neutron beam. This advanced measurement system enables us to obtain a detailed two-dimensional distribution map of thermal neutrons transmitted through spent fuel boards with varying burnup depths. By analyzing the grayscale intensity patterns in these maps, we can accurately evaluate the burnup degree within the simulated spent fuel plate. Furthermore, we establish a correlation between the transmitted thermal neutron count in the imaging field and the burnup depth of the spent fuel veneer. This allows for precise determination of burnup depth through the analysis of the two-dimensional distribution of transmission thermal neutron intensity. Our findings demonstrate the feasibility of a scheme for detecting the burnup depth in spent fuel boards based on transmission thermal neutron imaging technology, and obtained a linear relationship between the neutron transmission count and the burnup depth of the spent fuel plate, laying a solid theoretical foundation for future research and development of testing equipment to assess burnup in spent fuel veneers.

Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network

Bearing fault diagnosis presents challenges, such as insufficient fault samples and significant fault data distribution variation in different bearing operating conditions. These problems cause traditional deep learning models to show poor generality and accuracy during bearing fault diagnosis. To address these challenges, this paper proposed a few-shot bearing fault diagnosis method based on an Ensemble Empirical Mode Decomposition (EEMD) parallel neural network and a relation network (RN). First, the original bearing fault vibration signal was decomposed by EEMD, while the decomposed signal components were processed via Short Time Fourier Transfor (STFT) to obtain a two-dimensional time-frequency feature map. Then, a parallel neural network was used for initial fault feature extraction, after which the extracted features were fused to construct a more accurate multi-dimensional fault feature map. A more precise fault feature vector was generated via the feature embedding module of the RN, and the fault features of the support and query sets were stitched to create a fault feature vector set. Finally, the relation module of the RN was used for the nonlinear distance determination of the fault feature vector set and to generate the relation score for the few-shot variable condition bearing fault diagnosis. In this paper, EEMD module is introduced into RN to construct multi-dimensional fault characteristics of the original fault signal. Original signal decomposition, STFT transformation and splicing effectively improve the randomness and blindness of convolution operations, improve the accuracy of fault feature extraction in RN, and thus improve the overall diagnostic performance of the model. The experimental results showed that the proposed model obtained higher diagnostic accuracy than the matching network (MN) and RN meta-learning fault diagnosis (MLFD) methods. The accuracy of fault diagnosis of the model in 5Way-Nshot is >80%, and the accuracy of fault diagnosis in 5Way-10shot is the highest at 95.2%.

Image compression encryption algorithm combining two-dimensional modular hyperchaotic map and compressed sensing

Recently, to offer better ensure for image privacy security, numerous new image encryption algorithms have been proposed. However, these algorithms still suffer from the problems of chaotic performance scarcity, low encryption effect, and high consumption of computational resources. To solve the above issues, we first construct a two-dimensional modular hyperchaotic map (2D-MHM). Then, we further develop an image encryption algorithm based on 2D-MHM and compressed sensing (CS). Several chaotic metrics verify the randomness and validity of 2D-MHM. These metrics include bifurcation diagram, Lyapunov exponent, initial value sensitivity, 0–1 test, and NIST test. Specifically, CS significantly reduces the ciphertext image size thereby reducing its resource consumption during transmission. Reality-preserving fractional DCT (RP-Fdct) diffusion is utilized to transform pixels into the frequency domain to enhance the encryption effect. Subsequently, lightweight index confusion and XOR diffusion further improve the algorithm security. The security of the algorithm is verified through various experiments. It is able to encrypt grayscale and color images of different sizes with good results. Notably, this algorithm also implements the encryption requirements for binary images. Due to our designs, it outperforms recently reported encryption algorithms in several areas, especially in reconstruction performance.

TWO-DIMENSIONAL HYPERCHAOTIC MAP FOR CHAOTIC OSCILLATIONS

This manuscript explores a two-dimensional hyperchaotic map for generating chaotic oscillations. Hyperchaotic maps are finding increasing applications in various scientific and technological fields due to the unique properties of their generated oscillations. The studied map, based on two interconnected piecewise-linear functions, is one of the simplest for generating oscillations with a predetermined distribution of values across a continuous parameter space. This simplicity allows for wide applicability in various contexts. The paper presents simulation results demonstrating control over the parameters of the dynamic modes. Building upon these modeling results, a two-dimensional hyperchaotic system is implemented using an electric circuit. The chosen map is attractive due to its inherent simplicity and ease of parameter control. By adjusting these parameters, the distribution of the generated signal's values can be manipulated. The circuit consists of two symmetrical sections connected via feedback loops, employing four amplifiers with variable gain. The gain values act as the circuit's implementation of the control parameters. Chaotic oscillations are generated by applying a delayed clock signal from an external square wave generator to circuit elements. The obtained experimental results exhibit excellent agreement with the simulation data.

A High-Efficiency Two-Layer Path Planning Method for UAVs in Vast Airspace

In response to the challenges associated with the inefficiency and poor quality of 3D path planning for Unmanned Aerial Systems (UAS) operating in vast airspace, a novel two-layer path planning method is proposed based on a divide-and-conquer methodology. This method segregates the solution process into two distinct stages: heading planning and path planning, thereby ensuring the planning of both efficiency and path quality. Firstly, the path planning phase is formulated as a multi-objective optimization problem, taking into account the environmental constraints of the UAV mission and path safety. Subsequently, the multi-dimensional environmental data is transformed into a two-dimensional probabilistic map. An improved ant colony algorithm is proposed to efficiently generate high-quality sets of headings, facilitating the preliminary heading planning for UAVs. Then, the three-dimensional environment of the heading regions is extracted, and an improved Dung Beetle algorithm with multiple strategies is proposed to optimize the three-dimensional path in the secondary layer accurately. The efficacy and quality of the proposed path planning methodology are substantiated through comprehensive simulation analysis.