Two-dimensional Matrix Research Articles

Bloom의 신교육목표분류체계를 활용한 2022 개정 특수교육 기본 교육과정 성취기준 분석

This study analyzed the basic curriculum achievement standards, proposed the direction of
 application of the 2022 revised basic curriculum which will be implemented from 2024, and further
 presented a view of how the basic curriculum achievement standards will be used in the school
 field. To this end, a total of 684 achievement standards for the 2022 revised basic curriculum for
 special education were analyzed using Bloom’s New Taxonomy of Educational Objectives. The main
 findings are as follows. First, the results of the entire knowledge type analysis are concentrated on
 factual knowledge and procedural knowledge, the results of the cognitive process type analysis are
 concentrated in ‘remember’ and ‘apply’. Second, the results of achievement standard knowledge
 types by subject showed the highest distribution of factual knowledge types in all subjects except
 physical education, career, and vocational subjects, while procedural knowledge and meta-cognitive
 knowledge in career and vocational subjects were the highest. In the case of cognitive process types,
 ‘remember,’ ‘apply,’ and ‘understand,’ and as a result of two-dimensional matrix analysis, the ‘realistic
 knowledge-remember’ type showed the highest distribution. Third, the results of achievement
 standard knowledge types by grade group showed the highest distribution of factual knowledge
 types in all grade groups, followed by procedural knowledge. As a result of the type of achievement
 standard cognitive process by grade group, the ‘remember’ cognitive type was the highest rate until
 elementary school group, however the ‘applying’ cognitive type was the highest rate in middle and
 high school groups, and the two-dimensional matrix analysis showed the highest ‘factual
 knowledge-remember’ type statement in all grade groups. Overall, like the 2015 revised basic
 curriculum of special education, although there is a biased distribution of specific knowledge types
 and cognitive processes in achievement standards, it is significant in that meta-cognitive knowledge
 types were highly distributed due to curriculum characteristics in career and vocational subjects.

DETERMINATION OF THE STRATEGIC BEHAVIOUR OF ENTERPRISES REGARDING THE IMPLEMENTATION OF SOCIALLY RESPONSIBLE PRACTICES

The article provides scientific and methodological provisions for determining the strategic behaviour of an enterprise to implement socially responsible practices, compliance with which ensures the objectivity of management decisions in the field of their socially responsible activities. In order to determine the strategic behaviour of an enterprise in terms of implementation of socially responsible practices, a scientific and methodological approach is substantiated, based on the analysis of the gap between the level of implementation of the Sustainable Development Goals (SDGs) by an enterprise and the activity of these processes at the industry level. To identify the implementation of the SDGs at enterprises, the implementation rate was used, calculated on the basis of a set of single indicators using the binary assessment method for organisational support, systematic implementation and the complexity of the results of socially responsible programmes and projects. For defining the activity of implementation of the principles of sustainable development in the practice of enterprises, the article proposes to use an integral indicator calculated on the basis of comparison of the rate of change of single indicators of costs related to socially responsible practices for enterprises of a particular industry and enterprises of the economy as a whole. For the purpose of definition of the enterprise's strategic position with regard to implementation of the principles of social responsibility, a two-dimensional matrix with 5×5 parameters was developed, built using complex indicators of implementation of the SDGs and activity of their implementation. Depending on the position held by the enterprise in the matrix "implementation of the SDGs - activity of implementation of the principles of sustainable development by enterprises in the industry", recommendations on the strategic behaviour of the enterprise in the field of implementation of socially responsible practices have been developed. The use of the developed scientific and methodological approach based on the data of enterprises operating in the food production sector allowed to offer recommendations on their optimal behaviour in the development of socially responsible programmes and projects.

Detection of ADHD Disorder in Children Using Layer-Wise Relevance Propagation and Convolutional Neural Network: An EEG Analysis

Purpose: Attention-Deficit-Hyperactivity-Disorder (ADHD) is a neurodevelopmental disorder that begins in early childhood and often persists into adulthood, causing personality issues and social behavior problems. Thus, detecting ADHD in its early stages and developing an effective therapy is of tremendous interest. This study presents a deep learning-based model for ADHD diagnosis in children. Materials and Methods: The 'First-National-EEG-Data-Analysis-Competition-with-Clinical-Application' dataset is used for this purpose. Following preprocessing, data is segmented into 3-second epochs, and frequency features are extracted from these epochs. The Fourier transform is applied to each channel separately, and the resulting two-dimensional matrix (channel×frequency) for each epoch is used as the Convolutional Neural Network's (CNN) input. The CNN is made up of two convolutional layers, two max pooling layers and two fully connected layers as well as the output layer (a total of 9 layers) for classification. To improve the method's performance, the output of the classification of each input variable is analyzed. In other words, the role of each channel/frequency in the final classification is being investigated using the Layer-wise Relevance Propagation (LRP) algorithm. Results: According to the results of the LRP algorithm, only efficient channels are employed as Convolutional Neural Network (CNN) inputs in the following stage. This method yields a final accuracy of 94.52% for validation data. In this study, the feature space is visualized, useful channels are selected, and deep structure capabilities are exploited to diagnose ADHD disorder. Conclusion: The findings suggest that the proposed technique can be used to effectively diagnose ADHD in children.

Tuning ORR selectivity of π-conjugated cobalt corroles from 2e- to 4e-

Efficient oxygen reduction reaction (ORR) is crucial for the performance of fuel cells and other electrochemical devices. Seeking for stable and electrochemically selective 2e- and 4e- ORR electrocatalyst is yet a challenge, making the design extremely important and outmost of interest. In this study, we demonstrate a method to tune ORR selectivity by adjusting the local cobalt density through oligo-/polymerization of π-conjugated cobalt(III) A2B and A3-corroles to linear and two-dimensional matrix structures. These heterogeneous catalysts exhibit remarkable physicochemical properties, fast charge transfer kinetics, electrochemical reversibility, and high durability. Unselective three-electron transfer kinetics with n = 2.5–2.9 in between −0.20 V and +0.40 V vs. RHE are detected with the π-conjugated cobalt(III) A2B- corrole polymers. Highly selective four-electron kinetics with n = 3.7 in between −0.20 V and +0.40 V vs. RHE are detected by employing the cobalt A3-corrole oligomers during the ORR catalysis.

Superfluid response of two-dimensional filamentary superconductors

Different classes of low-dimensional superconducting systems exhibit an inhomogeneous filamentary superconducting condensate whose macroscopic coherence still needs to be fully investigated and understood. Here, we present a thorough analysis of the superfluid response of a prototypical filamentary superconductor embedded in a two-dimensional metallic matrix. By mapping the system into an exactly solvable random impedance network, we show how the dissipative (reactive) response of the system non-trivially depends on both the macroscopic and microscopic characteristics of the metallic (superconducting) fraction. We compare our calculations with resonant microwave transport measurements performed on LaAlO_33/SrTiO_33 heterostructures over an extended range of temperatures and carrier densities finding that the filamentary character of superconductivity accounts for unusual peculiar features of the experimental data.

APPLICATION OF DEEP LEARNING CROP CLASSIFICATION MODEL BASED ON MULTISPECTRAL AND SAR SATELLITE IMAGERY

Abstract. Classifying crops using satellite data is a challenge, especially since most crops have similar growth cycles. Due to their different characteristics and chlorophyll content, different crops exhibit subtle differences in their reflectance spectra. This study uses a data-driven approach to build a series of deep learning models to classify 36 different land covers in Steele County and Traill Country, North Dakota, US. A Google Earth Engine workflow was implemented to generate a composite layer containing Sentinel 1 and Sentinel 2 satellite data and surface crop data over the study area. 200,000 sample points were generated on this layer, 140,000 for training dataset, 30,000 for validation dataset and 30,000 for testing dataset. Each sample point contains the values of 12 months of SAR and spectral data. In this way, a two-dimensional feature matrix of the time dimension and spectral band dimension (bands refer to specific wavelengths of data in remote sensing imagery and other type of data like NDVI) is generated for each sample point. The training dataset of the model is composed of the feature matrix of these sample points, and the surface crops as labels correspond to the feature matrix. Since this is a dataset with two-dimensional features, this research uses four deep learning models: Dense Neural Network (DNN), Long short-term memory (LSTM), Convolutional neural network (CNN) and Transformer. Among them, the Transformer model based on the self-attention mechanism performed the best, with a comprehensive accuracy rate of 85%, and the classification accuracy rate of crops with more than 2,000 sample points in the training data set reached more than 90%.

Toward the balance between computational cost and model performance for the void detection of concrete-filled steel tubular structure using one-dimensional Mel-frequency cepstral coefficients and ensemble machine learning

Deep learning (DL) based percussion-acoustic methods have gained attention, but their multi-layer architectures and iterative processes increase computational time and power. This paper proposes a lightweight concrete-filled steel tubular (CFST) void detection method using Mel-frequency cepstral coefficient (MFCC) algorithm and ensemble machine learning. A global averaging pooling (GAP) layer was applied to downscale the two-dimensional MFCC matrix to one-dimensional (1D) time-frequency parameters. Combined with ensemble machine learning, lightweight models that require fewer model parameters, less computation, and smaller memory usage are constructed. Validation experiments were carried out on a CFST specimen with different depths of voids (0, 30, 50, and 80 mm). Robustness tests, considering tap intervals, sampling frequencies, and background noise, were performed by using data augmentation strategies. Two studies investigate feature extraction methods: the selection of the time-frequency analysis algorithm (MFCC or short-time Fourier transform) and the comparison of dimensionality reduction operation (average, maximum, and minimum pooling layers). Comparative analyses with a 1D dilated convolutional neural network (1D dilated CNN) reveal that the MFCC-based ensemble machine learning achieves excellent test accuracy on the raw signal dataset and better generalization capability on the reconstructed signal dataset. Moreover, the ensemble machine learning model exhibits significantly higher computational efficiency compared to DL models. Specifically, the training time of random forest is 17,510 times faster than the 1D dilated CNN. Therefore, the proposed lightweight CFST void detection method, incorporating 1D MFCC parameters and ensemble machine learning, achieves high model accuracy with low computational cost and holds promising potential for practical CFST applications.

Atmospheric lepton fluxes via two-dimensional matrix cascade equations

Atmospheric lepton fluxes via two-dimensional matrix cascade equations

Therapeutic drug monitoring mediated by the cooperative chemical and electromagnetic effects of Ti3C2TX modified with Ag nanocubes

It is pivotal for the credible utilization of surface-enhanced Raman scattering (SERS) technique in clinical drug monitoring to exploit versatile substrates with dependable quantitative detection and robust recognition abilities. Herein, a commendable electromagnetic-chemical dual-enhancement SERS substrate dependent on Ti3C2TX and Ag nanocubes (Ag NCs) was fabricated for the precise quantification of ritonavir and ibrutinib in serum. Specifically, it was revealed that numerous electromagnetic “hotspots” emerged nearby the extremely tiny nanogaps among the intimately clustered Ag NCs, which also acted as optimal channels to facilitate effective photo-induced charge transfer (PICT) between the two-dimensional Ti3C2TX matrix and target molecules. The cooperation between electromagnetic and chemical effects yielded a satisfactory enhancement factor (EF) of 4.77 × 107 for the composite substrate. Benefiting from the remarkable sensitivity of the Ti3C2TX/Ag NCs composite substrate, the low limit of detection (LOD) at 10−6 mg/mL was successfully attained, along with exceptional recoveries of exceeding 90% for ritonavir and ibrutinib in serum. Considering its reliability and simplicity, our strategy holds immense promise for its utilization in efficient monitoring and identification of clinical blood drug concentration.

DA-CapsNet: A multi-branch capsule network based on adversarial domain adaption for cross-subject EEG emotion recognition

Due to inter-individual variances, cross-subject electroencephalogram (EEG)-based emotion recognition is a challenging task. In this paper, we construct a multi-branch Capsule network (named DA-CapsNet) based on domain adaptation to improve the performance of cross-subject EEG emotion recognition. To fully capture the various intensity characteristics of a single emotion, firstly, DA-CapsNet decomposes the source and the target domain EEG signals into four frequency bands and homomorphically groups the data in each band, and then extracts the differential entropy (DE) features for each group separately. Taking into account the spatial arrangement of the electrodes, the DE features are mapped into a two-dimensional matrix to form a homomorphic difference cube sequence (HDCS). Second, to enhance the feature information of the same emotion and accelerate the run efficiency of the network, a parallel structured multi-branch primary Capsual network (CapsNet) is constructed in this paper. The multi-branch primary CapsNet can effectively extract the aforementioned sequence discriminative features and fuse them as the input features of the capsule emotion classifier. Finally, to lessen inter-domain distribution discrepancies, we brought adversarial domain adaptation to improve the performance of cross-subject emotion recognition. Numerous tests are run on the three public datasets of EEG, and the results show that the proposed algorithm in this paper works well.

Research on tool condition monitoring (TCM) using a novel unsupervised deep neural network (DNN)

In order to improve the recognition precision and accuracy of tool wear monitoring, an unsupervised deep neural network (DNN) based on stack denoising autoencoder (SDA) is proposed. After feature extraction and selection, the stack denoising automatic coding network reduces the dimensionality of the feature vector. On this basis, principal component analysis (PCA) and T-distributed random neighbor embedding (t-SNE) are used to reduce the dimensionality of the features twice, and finally a simple two-dimensional feature matrix is obtained. Finally, the deep neural network model of SDA is established by adding SoftMax regression layer, and the tool wear monitoring results are taken as new labeled data, and the deep neural network parameters are fine-tuned by secondary backpropagation. The experimental results show that the proposed method can learn adaptively and obtain effective feature expression, and the tool wear state recognition results are highly accurate. The proposed method can effectively identify the tool wear state.

Development of an algorithm for the reasoned selection of machines for leather garments manufacturing

The technology of processing clothing elements is very mobile and changes with the appearance of new materials and equipment. The selected design and technological solutions that ensure the conformity of aesthetic properties and requirements can be satisfied through continuous improvement of sewing production technologies. This study aims to develop an algorithm for choosing the optimal equipment, taking into account the material parameters and the permissible requirements for the technological process of manufacturing sewing products from artificial leather. As a result of the analysis of the range of sewing machines and manufacturing companies, Juki sewing machines were chosen to build a complex matrix of machine equipment. A morphological scheme of the selection process and a mathematical description of the two-dimensional matrix of elements have been developed. That has made it possible to reflect the simultaneous consideration of all process components and their influence on the technological parameters of the machine. The database of the parameters of the equipment selection system was built in the form of matrix elements: a matrix of the type of operations, a matrix of material coatings, a matrix of the base of materials, a matrix of machine assignments, and a matrix of qualifications. Each matrix is a production rule for making a decision at a separate selection stage. The operation matrix was constructed using the TechLab mobile application, which includes 50 processing schemes for artificial leather products. The analysis of the schemes has made it possible to determine the frequency of occurrence of types of seams for processing artificial leather products: 1.01.01 (28.75 %), 2.01.01 (16.75 %), 1.06.02 (11.00 %), 2.02.01 (15.50 %), 5.01.01 (24.25 %), 6.02.01 (3.75 %). The mathematical notation of the algorithm for choosing the optimal machine equipment has made it possible to visualize the structure of the process using a graph. The graph was built using Gephi. Such a notation takes into account the qualification of the worker, the type of technological operation, and the material’s properties, including the material’s thickness, coating, and base.

Research on mechanical property prediction of hot rolled steel based on lightweight multi-branch convolutional neural network

Various process parameters and chemical compositions affect the crystal phase structure of hot-rolled steel. The real-time prediction of mechanical properties is a key problem in this field. Implementing accurate, effective, real-time, and lightweight prediction according to its technological process is of great significance. Therefore, to solve the problem of weak characterization ability of the original data, the Gramian Angular Field method is proposed to transform the original data into a two-dimensional matrix to enrich the correlation features between the data. At the same time, to solve the high real-time requirements of compact tasks and improve the accuracy of the model, a combination module of the multi-branch enhanced convolutional kernel and adaptive bottleneck layer is proposed, which can improve the performance of the model and balance the relationship between lightweight. Experiments in this paper show that compared with similar models proposed in other literature, the optimal hyperparameter structure model proposed in this paper has higher prediction accuracy and smaller model scale, and can achieve higher prediction performance in a wide range of applications.

Evaluating the Effect of Voice Quality Covariance on Auditory-Perceptual Evaluation Using a Novel Two-Dimensional Magnitude Estimation Task.

Most people with dysphonia present with voices that vary along more than one voice quality (VQ) dimension. This study sought to examine the effect of covariance between breathy and rough VQ in natural voices. A two-dimensional matrix of 16 /a/ vowels was selected such that two VQ dimensions (breathiness and roughness) were sampled on a 4-point severity scale (none, mild, moderate, and severe). Ten listeners evaluated 480 stimuli (16 stimuli × 10 repetitions × 3 blocks) on one-dimensional magnitude estimation (1DME) tasks and a novel two-dimensional magnitude estimation (2DME) task that allowed for simultaneous measurement of breathiness and roughness. Data indicated high intra- and interrater reliabilities for both breathiness and roughness in the 2DME and 1DME tasks. Correlation analyses revealed a strong correlation between 2DME and 1DME judgments for breathiness and roughness (r > .95). There was also a minimal correlation between breathy and rough VQ in the 2DME task (r < .10). Covarying roughness or breathiness had less impact on the perception of the other VQ in natural dysphonic voices in 2DME compared to 1DME. An understanding and quantification of the perceptual interactions among the dimensions will aid in the refinement of computational models and in the establishment of the validity of clinical scales for VQ perception.

Research on the development strategy of integration of Chinese traditional culture and tourism development oriented to a matrix model

Abstract Based on its evolutionary history, this paper explores the different manifestations of traditional culture in tourism development. The matrix decomposition of the spatial elements of tourism development is used to study the coupling relationship between tourism space and traditional culture. Finally, the evaluation index system of the tourism development degree is constructed, and the two-dimensional matrix model is used to realize the integration measurement of traditional culture inheritance and tourism development. In the empirical study, the evaluation index of cultural industry development in Liaoning province increased year by year from 2011 to 2020, reaching a stage peak of 0.623189 in 2015, and the coupling degree of the culture and tourism industry in Liaoning province was greater than 0.92. The measurement model established in this study effectively promotes the integrated development of traditional culture tourism.

Research on a novel fault diagnosis method for gearbox based on matrix distance feature

Aiming at the problem of fault diagnosis and classification of rolling bearing and gear of gearboxes, a novel method based on matrix distance features of Gramian angular field (GAF) image is proposed based on sliding window compressible GAF transformation. The method converts the one-dimensional fault signal into a two-dimensional feature matrix and constructs the discrimination matrix of each fault category by establishing the mean value of the feature matrix of a priori samples. For the new sampled signal, after converting it into a two-dimensional feature matrix, the feature matrix is obtained. The fault classification is carried out by using the matrix distance between feature matrix and the discrimination matrix of each category. The method is validated by the test data of Case Western Reserve University and the acoustic emission data from a gearbox test bench. The classification accuracy is 99.17% and 95.71%, which presented the feasibility and effectiveness of the novel method proposed in this paper.

Explosion Risk of Metal Powder Used in Additive Manufacturing

The explosive characteristic parameters of additive manufacturing (AM) dust were systematically studied. A systematic experimental study was performed on the characteristic parameters of dust explosions fueled by metal powder for AM, to obtain the distribution pattern of those parameters for four types of AM dust samples. Based on experimental data, the dust-explosive severity was classified via two-dimensional matrix analysis in terms of Pmax and Kst, and the dust-explosive sensitivity was classified via three-dimensional matrix analyses of the MIE, MIT, and MEC. Finally, a dust-explosive risk assessment model of AM metal powder was established based on comprehensive considerations of the dust-explosive severity and sensitivity, and four types of AM dust sample were applied empirically. The risk assessment method can provide a scientifically credible and effective basis for assessing and improving the AM process based on powder spreading.

Image encryption algorithm based on hyperchaotic system and dynamic DNA encoding

Since the existing DNA encryption algorithms only have fixed DNA coding and decoding rules and a single algorithm that cannot meet more complex and more secure encryption requirements, an image encryption algorithm based on hyperchaotic system and dynamic DNA coding is proposed. The algorithm proposes two new operation methods in the DNA operation rules, namely the equal-additive column transformation and the equal-subtractive column transformation. which combine the SHA-256 function and the external key to generate the initial value of the chaotic system to ensure that key and plaintext of algorithm are correlated. In the encryption process, the plaintext image is firstly converted into a two-dimensional matrix for rotation and scrambling. Then the chaotic sequence generated by the hyperchaotic Chen system is used to perform DNA dynamic encoding, decoding and operation on the scrambled matrix and block matrix generated by logistic chaotic sequence. Finally, the encrypted matrix is scrambled in one dimension. Simulation results show that the improved encryption algorithm has a larger key space, breaks the strong correlation between image pixel layers, and can effectively resist multiple attacks.

Sunlight-driven photocatalytic degradation of Norfloxacin antibiotic in wastewater by ZnSe microsphere functionalized RGO composite

The release of hazardous pollutants into the water, such as dyes, phenolic compounds, pesticides, different heavy metals, and antibiotics is the primary cause of environmental pollution. The solar energy-based photocatalysis technique has the potential to eliminate pollutants from the aquatic environment. ZnSe is a significant and commendable II-VI group photocatalyst having 2.82 eV direct band gap energy. Its performance towards degrading antibiotics in the presence of light was improved by anchoring it on the Reduced graphene oxide (RGO) matrix. Solution-processable RGO-ZnSe composites were characterized by X-ray diffraction, scanning electron microscopy, and transmission electron microscopy. Several spectroscopic studies like Raman spectroscopy, ultraviolet-visible spectroscopy, and photoluminescence spectroscopy were employed to illustrate the optical properties of the composites. The findings demonstrate that the ZnSe microsphere was supported effectively onto the two-dimensional RGO matrix. The performance of as-synthesized RGO-ZnSe composites was assessed based on the photocatalytic degradation of the antibiotic Norfloxacin in the presence of simulated solar light. An optimized RGO-ZnSe composite degrades approximately 83.5% of Norfloxacin in 40 min, while controlled-ZnSe photocatalysis only yields 33% in an identical experimental condition. The optimized RGO-ZnSe achieves the highest degradation reaction rate constant (k) of Norfloxacin (k = 0.057 min−1), and the k value is 4.38 times greater compared to the controlled-ZnSe microsphere. The light-dependent catalytic degradation mechanism was examined by employing scavenger experiments which gave away the responsible reactive radicals. Our results establish that the photo-generated hole has the leading role in the degradation of Norfloxacin in our system. In addition to that, the oxide radical and hydroxyl radical has also an important role in the degradation of Norfloxacin under our experimental condition. The finding suggests that the addition of RGO has enhanced photocatalytic performance and cyclic stability by preventing photo-generated electron-hole pair recombination. The photocatalytic oxidative deactivation mechanism of the Norfloxacin antibiotic was proposed. Our study offers new perceptiveness for the design of efficient solution-processable solar light-responsible photocatalysts for environmental pollution remediation.

Spectrum Sensing Method Based on Residual Dense Network and Attention.

To address the problems of gradient vanishing and limited feature extraction capability of traditional CNN spectrum sensing methods in deep network structures and to effectively avoid network degradation issues under deep network structures, this paper proposes a collaborative spectrum sensing method based on Residual Dense Network and attention mechanisms. This method involves stacking and normalizing the time-domain information of the signal, constructing a two-dimensional matrix, and mapping it to a grayscale image. The grayscale images are divided into training and testing sets, and the training set is used to train the neural network to extract deep features. Finally, the test set is fed into the well-trained neural network for spectrum sensing. Experimental results show that, under low signal-to-noise ratios, the proposed method demonstrates superior spectral sensing performance compared to traditional collaborative spectrum sensing methods.