Input Data Samples Research Articles

Quantum next generation reservoir computing: an efficient quantum algorithm for forecasting quantum dynamics

Next Generation Reservoir Computing (NG-RC) is a modern class of model-free machine learning that enables an accurate forecasting of time series data generated by dynamical systems. We demonstrate that NG-RC can accurately predict full many-body quantum dynamics in both integrable and chaotic systems. This is in contrast to the conventional application of reservoir computing that concentrates on the prediction of the dynamics of observables. In addition, we apply a technique which we refer to as skipping ahead to predict far future states accurately without the need to extract information about the intermediate states. However, adopting a classical NG-RC for many-body quantum dynamics prediction is computationally prohibitive due to the large Hilbert space of sample input data. In this work, we propose an end-to-end quantum algorithm for many-body quantum dynamics forecasting with a quantum computational speedup via the block-encoding technique. This proposal presents an efficient model-free quantum scheme to forecast quantum dynamics coherently, bypassing inductive biases incurred in a model-based approach.

Classification of expansive soils among problematic soils in the brazilian semiarid from artificial neural networks

The climatological and geomorphological conditions of the Brazilian semiarid region favor the formation of potentially expansive soils. The development of neural networks to identify and classify expansive soils in Pernambuco, and the need to export to soils across the semiarid region of Brazil is investigated in this study using a Multi-Layer Perceptron (backpropagation). The neural network was developed using 87 experimental data points, divided into three groups. The Training Group, consisting of 53 samples, using data inputs: sand percentage, clay percentage, plasticity index, activity index, and geological, pedological, and climatological classification. Selection Group, made up of 17 samples, was used to select the best network architecture, genetics algorithms formed of 7 inputs and 3 hidden nodes. The Test Group used 17 samples to evaluate the predictive accuracy of the expansion potential, obtaining an accuracy rate of 88,2%. This network was validated by applying it to 67 samples of problematic soils of the collapsible, expansive, and soft type from the Brazilian semiarid region, reaching an accuracy of 76,1%. Probabilistic Neural Networks were found to be efficient in evaluating the behavior of expansive soils, with the ability to deal with the absence of sample input data, demonstrating the ability to capture movement trends in the expansion of the soil surface, indicating the functions that introduce the effects of the composition potential on the expansion behavior, and determining the limit values of each of the input variables for the samples from the database used.

Research on Transformer Fault Diagnosis by WOA‐SVM Based on Feature Selection and Data Balancing

AbstractOil‐immersed transformers as one of the most important equipment in the power system, the fault prediction of it in advance can effectively reduce the subsequent harm. Aiming at the selection of input features and data sample imbalance in the transformer fault diagnosis model, this paper adopts the recursive feature elimination (RFE) method combined with SMOTETomek comprehensive sampling method to optimize the above problems. First, RFE is used to traverse all the features and filter the optimal combination of them as input features, then SMOTETomek is used to perform balancing operation on the samples of the train set, and finally, whale optimization algorithm (WOA) is used to find the best hyperparameters for support vector machine (SVM), and the results are compared with the diagnostic models operated without processing and after single processing operation, respectively. After several sets of experiments, it is proved that the optimized comprehensive fault diagnosis model performs better on the test set than both the untreated and the singly processed models, which proves the effectiveness of the methodology used in this paper. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Intuitionistic fuzzy twin proximal SVM with fuzzy hyperplane and its application in EEG signal classification

Twin support vector machine (TSVM) is a contemporary machine learning technique to tackle classification and regression problems. However, TSVM lacks the ability to differentiate between support vectors and noises since it neglects the positional information of input data samples, and hence it is sensitive to noises. Additionally, it fails to consider uncertainties associated with the data, which reduces its capacity for generalization. To address these drawbacks, we propose a novel fuzzy hyperplane based intuitionistic fuzzy twin proximal support vector machine. The first significant feature of the proposed approach is that it gives an intuitionistic fuzzy number based on the relevance to each data vector. This efficiently reduces the impact of noise and outliers by leveraging the local neighborhood information within the data points by incorporating membership and non-membership weights. Secondly, all the parameters present in the model are fuzzy variables, including the offset term and the elements of the normal vector. The suggested fuzzy hyperplane successfully reflects the inherent ambiguity prevalent in real-world categorization problems by reflecting vagueness in the input data through the use of fuzzy variables. The model’s efficiency is enhanced by solving two systems of linear equations to obtain two non-parallel classifiers rather than solving two quadratic programming problems as in standard TSVM. Utilizing non-linear kernel functions within the feature space enables the method to effectively identify complex patterns or non-linear relationships within the datasets. In order to demonstrate the effectiveness of the suggested approach, extensive computer experiments have been conducted on a set of eighteen benchmark datasets with both linear and non-linear kernels. In addition, rigorous statistical analysis, including Friedman and post-hoc Nemenyi tests, have been employed to assess the significance of the observed performance differences. Furthermore, we also performed numerical experiments utilizing linear, Gaussian, and polynomial kernels to classify electroencephalogram (EEG) signals. The outcomes of the experiment are analyzed in terms of average accuracy, processing time, and F-measure. The results demonstrate that the proposed method outperforms existing methods and achieves better generalization.

Seismic data extrapolation based on multi-scale dynamic time warping

Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging, playing a crucial role in seismic data processing. In seismic data reconstruction, a common scenario involves a significant distance between the source and the first receiver, which makes it unattainable to acquire near-offset data. A new workflow for seismic data extrapolation is proposed to address this issue, which is based on a multi-scale dynamic time warping (MS-DTW) algorithm. MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset (t−x) domain data. Using the time-shift calculated by the MS-DTW as the basic input, predict the two-way traveltime (TWT) of other traces based on the TWT of the reference trace. Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients. Extract amplitude information from the TWT curve, fit the amplitude curve, and extrapolate the amplitude using polynomial coefficients. The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information. It applies to both isotropic and anisotropic media. The effectiveness of the workflow was verified through synthetic data and field data. The results show that compared with the method of predictive painting based on local slope, this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.

Robust Spherical Laplacian Embedding.

Laplacian embedding (LE) aims to project high-dimensional input data samples, which often contain nonlinear structures, into a low-dimensional space. However, existing distance functions used in the embedding space fail to provide discriminative representations for real-world datasets, especially those related to text analysis or image processing. Cosine similarity measurements are advantageous in dealing with sparse data but are fragile to the impact of outliers and noise from samples or data features. In this work, we propose robust spherical LE (RS-LE), a powerful method that builds the LE on a novel metric that unifies the Euclidean distance and cosine similarity in a spherical space using a robust lp,p -norm. We introduce an efficient iterative algorithm, the proximal alternating linearized minimization (ALM) method, to solve the difficult optimization problem that arises from the new metric, which is neither convex nor smooth. This algorithm allows the solution to achieve both global and objective convergence. Our findings are presented in rigorous theoretical analysis and validated in experiments.

Recall-Oriented Continual Learning with Generative Adversarial Meta-Model

The stability-plasticity dilemma is a major challenge in continual learning, as it involves balancing the conflicting objectives of maintaining performance on previous tasks while learning new tasks. In this paper, we propose the recalloriented continual learning framework to address this challenge. Inspired by the human brain’s ability to separate the mechanisms responsible for stability and plasticity, our framework consists of a two-level architecture where an inference network effectively acquires new knowledge and a generative network recalls past knowledge when necessary. In particular, to maximize the stability of past knowledge, we investigate the complexity of knowledge depending on different representations, and thereby introducing generative adversarial meta-model (GAMM) that incrementally learns task-specific parameters instead of input data samples of the task. Through our experiments, we show that our framework not only effectively learns new knowledge without any disruption but also achieves high stability of previous knowledge in both task-aware and task-agnostic learning scenarios. Our code is available at: https://github.com/bigdata-inha/recall-orientedcl-framework.

Multi-Index Fusion Debris Flow Early Warning Model Based on Spatial Interpolation and Support Vector Machine

Debris flow early warning is an effective method to prevent major disasters, so a multi-index fusion debris flow early warning model based on spatial interpolation and a support vector machine is designed. Aiming at the discrete rainfall data in the study area, the collaborative Kriging spatial interpolation method based on Kriging spatial interpolation is adopted to process the rainfall data into multi-index fused surface data. The rainfall data after spatial interpolation are used as the input sample data of the support vector machine early warning model, and the optimal parameters of the support vector machine are calculated by the sea squirt algorithm, and then the debris flow early warning results are output. After experimental analysis, the model can obtain rainfall surface data. After calculation by the model, the accuracy of the early warning probability of debris flow is improved, and the early warning result is consistent with the actual result of debris flow.

LSTM‐based post‐processing for noise reduction in SVD‐based particulate matter digital twinning on lightweight embedded devices

AbstractAmong various applications of digital twins, measuring particulate matter in the context of air pollution has become increasingly important due to growing interest in atmospheric environments. By obtaining particle count values from analogue‐to‐digital converter raw data through photodiode and calibrating them, particulate matter values can be acquired. The singular‐value–decomposition is efficient transfer function learning method. However, due to the linear nature of the transfer function of singular‐value–decomposition, it also conveys noise information, necessitating post‐processing to suppress noise. This study proposes a method that utilizes long short‐term memory neural networks to effectively stabilize noise based on the output of continuous dust sensor particulate matter distributions. Using the singular value decomposition based particulate matter shows relative root‐mean‐square error (4.4761, 100%) as a reference, the proposed long short‐term memory post‐processing demonstrates an improved result (2.9328, 65.52%) compared to other post‐processing method: the mean filter (3.6704, 82.00%), low‐pass filter (3.7719, 84.27%) and Kalman filter (3.5550, 79.42%). Furthermore, to address the limitation of initial delay in long short‐term memory to achieve stable data output, a method of iteratively training the initial input data sample is proposed. For the initial input data, the non‐initialization approach exhibited a relative error of 87.57% and it took around 5 samples to predict stable output. In contrast, the proposed iterative training method, applied five times to the initial data sample, achieved a relative error of 9.51% and immediately obtained stable data.

Tactics analysis and evaluation of women football team based on convolutional neural network

In order to realize the process of player feature extraction and classification from multi-frequency frame-changing football match images more quickly, and complete the tactical plan that is more conducive to the game, this paper puts forward a method for analyzing and judging the tactics of women’s football team based on Convolutional Neural Network (CNN). By extracting the players’ performance in recent training and competition from continuous video frame data, a multi-dimensional vector input data sample is formed, and CNN is used to analyze the players’ hidden ability before the game and the players’ mistakes in different positions on the field to cope with different football schedules. Before the formal test, 10 games of 2021–2022 UEFA Women’s Champions League were randomly selected and intercepted to train the CNN model. The model showed excellent accuracy in the classification of image features of various football moves and goal angles, and the overall classification accuracy of each category exceeded 95%. The accuracy of classifying a single match is above 88%, which highlights the reliability and stability of the model in identifying and classifying women’s football matches. On this basis, the test results show that: according to the analysis of players’ personal recessive ability before the game, after model image recognition and comparison, the difference between the four scores of players’ personal recessive ability with CNN mode and the manual score of professional coaches was smaller, and the numerical difference was within the minimum unit value, and the numerical calculation results were basically the same. According to the analysis of players’ mistakes in different positions on the field, CNN was used to monitor the real-time mistakes. It was found that the two players in the forward position made the highest mistakes, and they were replaced by substitute players at 73.44 min and 65.28 min after the team scored and kept the ball, respectively. After the substitute players played, the team’s forward position mistake rate decreased obviously. The above results show that CNN technology can help players get personal recessive ability evaluation closer to professional evaluation in a shorter time, and help the coaching team to analyze the real-time events better. The purpose of this paper is to help the women’s football team complete the pre-match tactical training, reduce the analysis time of players’ mistakes in the game, deal with different opponents in the game and improve the winning rate of the game.

MK-BMC: a Multi-Kernel framework with Boosted distance metrics for Microbiome data for Classification.

Research on human microbiome has suggested associations with human health, opening opportunities to predict health outcomes using microbiome. Studies have also suggested that diverse forms of taxa such as rare taxa that are evolutionally related and abundant taxa that are evolutionally unrelated could be associated with or predictive of a health outcome. Although prediction models were developed for microbiome data, no prediction models currently exist that use multiple forms of microbiome-outcome associations. We developed MK-BMC, a Multi-Kernel framework with Boosted distance Metrics for Classification using microbiome data. We propose to first boost widely used distance metrics for microbiome data using taxon-level association signal strengths to up-weight taxa that are potentially associated with an outcome of interest. We then propose a multi-kernel prediction model with one kernel capturing one form of association between taxa and the outcome, where a kernel measures similarities of microbiome compositions between pairs of samples being transformed from a proposed boosted distance metric. We demonstrated superior prediction performance of (i) boosted distance metrics for microbiome data over original ones and (ii) MK-BMC over competing methods through extensive simulations. We applied MK-BMC to predict thyroid, obesity, and inflammatory bowel disease status using gut microbiome data from the American Gut Project and observed much-improved prediction performance over that of competing methods. The learned kernel weights help us understand contributions of individual microbiome signal forms nicely. Source code together with a sample input dataset is available at https://github.com/HXu06/MK-BMC.

Learning-Based Adaptive Optimal Control of Linear Time-Delay Systems: A Policy Iteration Approach

This paper studies the adaptive optimal control problem for a class of linear time-delay systems described by delay differential equations (DDEs). A crucial strategy is to take advantage of recent developments in reinforcement learning (RL) and adaptive dynamic programming (ADP) and develop novel methods to learn adaptive optimal controllers from finite samples of input and state data. In this paper, the data-driven policy iteration (PI) is proposed to solve the infinite-dimensional algebraic Riccati equation (ARE) iteratively in the absence of exact model knowledge. Interestingly, the proposed recursive PI algorithm is new in the present context of continuous-time time-delay systems, even when the model knowledge is assumed known. The efficacy of the proposed learning-based control methods is validated by means of practical applications arising from metal cutting and autonomous driving.

ПРОГРАММНЫЙ КОМПЛЕКС РАСЧЕТА КОЛИЧЕСТВЕННЫХ ХАРАКТЕРИСТИК ОБЪЕКТОВ В УСЛОВИЯХ ОГРАНИЧЕННОГО НАБОРА СЛАБООПРЕДЕЛЕННЫХ ДАННЫХ

The article describes the original architecture and provides examples of the operation of the software package for calculating the predicted values of weakly defined data using the example of predicting the level of metals in human biosubstrates depending on the content of metals in drinking water, features of the area of residence, and physiological characteristics of a person. Weakly defined data are rare or hard-to-reach data whose measurements are irregular and made with errors. The complex includes a neural network calculation method and a calculation method based on a cluster modification of the k-nearest neighbors method. The choice of one method is based on the analysis and assessment of the volume of the input data sample. For the k-nearest neighbors method, a preliminary clustering procedure with a quantitative assessment of its quality is provided. To exclude or correct statistical outliers caused by measurement errors, a procedure for cleaning data from outliers in manual or automatic mode is proposed. The software package allows you to load data from MS Office files and unload the results as text tables. In addition to batch loading, it is possible to add, modify, or delete an individual data vector manually. To train the models, the input data is divided into a training and test set of 85% and 25%. After training, each model can be saved. Working with the complex is simplified by an intuitive, user-friendly interface implemented entirely in Russian.

Intelligent Prediction of Drilling Rate of Penetration Based on Method-Data Dual Validity Analysis

Summary The rate of penetration (ROP) is a critical parameter in drilling operations, essential for optimizing the drilling process and enhancing drilling speed and efficiency. Traditional and statistical models are inadequate for predicting ROP in complex formations, as they fail to conduct a comprehensive analysis of method validity and data validity. In this study, geological conditions parameters, mechanical parameters, and drilling fluid parameters were extracted as prediction parameters, and an intelligent ROP prediction method was constructed under method-data dual validity analysis. The effectiveness of the ROP prediction method is studied by comparing five machine learning algorithms. The data validity of ROP prediction is also studied by changing the input data type, input data dimension, and input data sampling method. The results show that the effectiveness of the long short-term memory (LSTM) neural network method was found to be superior to support vector regression (SVR), backpropagation (BP) neural network, deep belief neural network (DBN), and convolutional neural network (CNN) methods. For data validity, the best input data type for ROP prediction is geological conditions parameters after principal component analysis (PCA) combined with mechanical parameters and drilling fluid parameters. The lower limit of input data dimension validity is seven input parameters, and the accuracy of prediction results increases with the increase of data dimension. The optimal data sampling method is one point per meter, and the error of the prediction result increases and then decreases with the increase of sampling points. Through step-by-step analysis of method validity, input data type, input data dimension, and input data sampling method, the range, size, and mean of error values of ROP prediction results were significantly reduced, and the mean absolute percentage error (MAPE) of the prediction results of the test set is only 18.40%, while the MAPE of the prediction results of the case study is only 11.60%. The results of this study can help to accurately predict ROP, achieve drilling speedup in complex formations, and promote the efficient development of hydrocarbons in the study area.

Analysis of the Influence Path of Confucianism in the Civic Education of Contemporary College Students in the Context of Big Data

Abstract Under the background of big data, college students’ Civic Education has realized the adjustment of teaching technology and educational paradigm, and the value of big data technology for college students’ Civic Education has become increasingly intuitive. In this paper, firstly, the data points designated for clustering are selected in the dataset as the initial clustering centroids. In response to the Euclidean distance treating variables equally, the coefficient of variation method is proposed for subjective weighting. The support vector machine for solving the two-class classification problem is obtained as a real-valued function to test the parallel efficiency of different clustering mining algorithms in the same experimental setting. Finally, for the training sample input data, it is concluded that the algorithm has a better clustering effect and improves the speed and accuracy of clustering. The feasibility of the proposed influence path is demonstrated by comparing it with the data application of the subjective empirical weighting method. The results show that 8% of students before the experiment thought that integrating Confucianism into Civic Education was valuable and meaningful, and after the experiment, the number of students who thought it was valuable and meaningful increased from 8% to 58%. It indicates that the integration of excellent traditional Confucianism into the ideological and political education of college students is very important to strengthen the ideological and political education of college students.

LQMPCS: Design of a Low-Complexity Q-Learning Model based on Proof-of-Context Consensus for Scalable Side Chains

Single-chained blockchains are being rapidly replaced by sidechains (or sharded chains), due to their high QoS (Quality of Service), and low complexity characteristics. Existing sidechaining models use context-specific machine-learning optimization techniques, which limits their scalability when applied to real-time use cases. Moreover, these models are also highly complex and require constant reconfigurations when applied to dynamic deployment scenarios. To overcome these issues, this text proposes design of a novel low-complexity Q-Learning Model based on Proof-of-Context (PoC) consensus for scalable sidechains. The proposed model initially describes a Q-Learning method for sidechain formation, which assists in maintaining high scalability even under large-scale traffic scenarios. This model is cascaded with a novel Proof-of-Context based consensus that is capable of representing input data into context-independent formats. These formats assist in providing high-speed consensus, which is uses intent of data, instead of the data samples. To estimate this intent, a set of context-based classification models are used, which assist in representing input data samples into distinctive categories. These models include feature representation via Long-Short-Term-Memory (LSTM), and classification via 1D Convolutional Neural Networks (CNNs), that can be used for heterogeneous application scenarios. Due to representation of input data samples into context-based categories, the proposed model is able to reduce mining delay by 8.3%, reduce energy needed for mining by 2.9%, while maintaining higher throughput, and lower mining jitters when compared with standard sidechaining techniques under similar use cases.

MARKOV NETWORK MODEL

Markov networks synthesized from the N-scheme of the Markov algorithm, developed to replace his well-known y-scheme, are studied theoretically from the standpoint of theories of algorithms and categories. The provisions of the theory of Markov algorithms are developed in the direction of synthesizing methods for the joint processing of words and morphism values that are occurrences in these words. In Markov networks, morphisms are weighted according to the method adopted in artificial neural networks. The material for the synthesis of Markov networks are diagrams of Markov occurrences of words into each other, obtained from input data samples, by classifying occurrences in the alphabet M = λιτφпо1…оx/αβδγχκμ. The Markov network model and the activation function of its morphemes are synthesized and subjected to scientific analysis. The idea of joint consideration of category theory and the theory of Markov algorithms is developed for the control channel, so this control channel is understood as an n-category associator introduced into the N-scheme of the Markov algorithm. In Markov networks, chain termination mechanisms are studied, using the control channel command, using combined methods of classifying words and morphisms, previously studied for the N-scheme of his algorithm, the evolution of categories formed by words is considered due to their occurrences in each other. For the method of synthesizing training samples from diagrams of Markov occurrences, a scientific substantiation of prescriptions for processing samples is carried out. For activation functions, examples of their implementation are considered. The categorical representation of the activation function is synthesized for Markov networks and subjected to a separate scientific analysis. For single-layer and multi-layer Markov networks of direct distribution, a block diagram has been developed. In Markov networks, the concept of zero-nets is introduced. As a promising direction for further theoretical research in Markov networks, multilayer Markov networks of indirect propagation are singled out, which have a conclusion that cannot be considered normal according to Markov. In the conclusions to the study, it is noted that Markov networks have a category-theoretic representation and can be synthesized from Markov zero-nets.

Packet Loss Compensation for VoIP through Bone‐Conducted Speech Using Modified Linear Prediction

In this paper, we compare air‐conducted (AC) speech with bone‐conducted (BC) speech for the purpose of utilizing them in packet loss concealment (PLC) for speech quality of voice over internet protocol (VoIP). Instead of the autocorrelation method of linear prediction (LP), which was utilized in the conventional PLC techniques, we employ the modified covariance (MC) method. The MC method provides accurate LP estimation from short input data samples and avoids the numerical problem the autocorrelation method suffers from. The lost frame is compensated from both forward and backward directions in which linear gain and weighting are applied. When BC speech is used as the input speech data for PLC in the case where the speech sender is in noisy environments, BC speech behaves more accurately than the corresponding AC speech, resulting in an excellent performance of the LP‐based PLC technique. This unveils a useful use of BC speech in speech information systems. Experiments show that in severe noise environments of AC speech‐to‐noise ratio being less than 10 dB, BC speech is superior to AC speech for PLC. It is also shown that the transmitted BC speech is more accurately reconstructed for PLC than transmitted AC speech is done. © 2023 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

A Derivative-Incorporated Adaptive Gradient Method for Federated Learning

As a new machine learning technique, federated learning has received more attention in recent years, which enables decentralized model training across data silos or edge intelligent devices in the Internet of Things without exchanging local raw data. All kinds of algorithms are proposed to solve the challenges in federated learning. However, most of these methods are based on stochastic gradient descent, which undergoes slow convergence and unstable performance during the training stage. In this paper, we propose a differential adaptive federated optimization method, which incorporates an adaptive learning rate and the gradient difference into the iteration rule of the global model. We further adopt the first-order moment estimation to compute the approximate value of the differential term so as to avoid amplifying the random noise from the input data sample. The theoretical convergence guarantee is established for our proposed method in a stochastic non-convex setting under full client participation and partial client participation cases. Experiments for the image classification task are performed on two standard datasets by training a neural network model, and experiment results on different baselines demonstrate the effectiveness of our proposed method.

Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications.

Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property prediction and brain connectome analysis. Importantly, recent works have shown that considering relationships between input data samples has a positive regularizing effect on the downstream task in healthcare applications. These relationships are naturally modeled by a (possibly unknown) graph structure between input samples. In this work, we propose Graph-in-Graph (GiG), a neural network architecture for protein classification and brain imaging applications that exploits the graph representation of the input data samples and their latent relation. We assume an initially unknown latent-graph structure between graph-valued input data and propose to learn a parametric model for message passing within and across input graph samples, end-to-end along with the latent structure connecting the input graphs. Further, we introduce a Node Degree Distribution Loss (NDDL) that regularizes the predicted latent relationships structure. This regularization can significantly improve the downstream task. Moreover, the obtained latent graph can represent patient population models or networks of molecule clusters, providing a level of interpretability and knowledge discovery in the input domain, which is of particular value in healthcare.