Features Of Artificial Neural Networks Research Articles

Revealing nonclassicality of multiphoton optical beams via artificial neural networks

The identification of nonclassical features of multiphoton quantum states represents a crucial task in the development of many quantum photonic technologies. Under realistic experimental conditions, a photonic quantum state gets affected by its interaction with several nonideal optoelectronic devices, including those used to guide, detect, or characterize it. The result of such noisy interaction is that the nonclassical features of the original quantum state get considerably reduced or are completely absent in the detected, final state. In this work, the self-learning features of artificial neural networks are exploited to experimentally show that the nonclassicality of multiphoton quantum states can be assessed and fully characterized, even in the cases in which the nonclassical features are concealed by the measuring devices. Our work paves the way toward artificial-intelligence-assisted experimental-setup characterization, as well as quantum state nonclassicality identification. Published by the American Physical Society 2024

Pseudo datasets explain artificial neural networks

AbstractMachine learning enhances predictive ability in various research compared to conventional statistical approaches. However, the advantage of the regression model is that it can effortlessly interpret the effect of each predictor. Therefore, interpretable machine-learning models are desirable as the deep-learning technique advances. Although many studies have proposed ways to explain neural networks, this research suggests an intuitive and feasible algorithm to interpret any set of input features of artificial neural networks at the population-mean level changes. The new algorithm provides a novel concept of generating pseudo datasets and evaluating the impact due to changes in the input features. Our approach can accurately obtain the effect estimate from single to multiple input neurons and depict the association between the predictive and outcome variables. According to computer simulation studies, the explanatory effects of the predictors derived by the neural network as a particular case could approximate the general linear model estimates. Besides, we applied the new method to three real-life analyzes. The results demonstrated that the new algorithm could obtain similar effect estimates from the neural networks and regression models. Besides, it yields better predictive errors than the conventional regression models. Again, it is worth noting that the new pipeline is much less computationally intensive than the SHapley Additive exPlanations (SHAP), which could not simultaneously measure the impact due to two or more inputs while adjusting for other features.

Motion sensitive network for action recognition in control and decision-making of autonomous systems.

Spatial-temporal modeling is crucial for action recognition in videos within the field of artificial intelligence. However, robustly extracting motion information remains a primary challenge due to temporal deformations of appearances and variations in motion frequencies between different actions. In order to address these issues, we propose an innovative and effective method called the Motion Sensitive Network (MSN), incorporating the theories of artificial neural networks and key concepts of autonomous system control and decision-making. Specifically, we employ an approach known as Spatial-Temporal Pyramid Motion Extraction (STP-ME) module, adjusting convolution kernel sizes and time intervals synchronously to gather motion information at different temporal scales, aligning with the learning and prediction characteristics of artificial neural networks. Additionally, we introduce a new module called Variable Scale Motion Excitation (DS-ME), utilizing a differential model to capture motion information in resonance with the flexibility of autonomous system control. Particularly, we employ a multi-scale deformable convolutional network to alter the motion scale of the target object before computing temporal differences across consecutive frames, providing theoretical support for the flexibility of autonomous systems. Temporal modeling is a crucial step in understanding environmental changes and actions within autonomous systems, and MSN, by integrating the advantages of Artificial Neural Networks (ANN) in this task, provides an effective framework for the future utilization of artificial neural networks in autonomous systems. We evaluate our proposed method on three challenging action recognition datasets (Kinetics-400, Something-Something V1, and Something-Something V2). The results indicate an improvement in accuracy ranging from 1.1% to 2.2% on the test set. When compared with state-of-the-art (SOTA) methods, the proposed approach achieves a maximum performance of 89.90%. In ablation experiments, the performance gain of this module also shows an increase ranging from 2% to 5.3%. The introduced Motion Sensitive Network (MSN) demonstrates significant potential in various challenging scenarios, providing an initial exploration into integrating artificial neural networks into the domain of autonomous systems.

A novel identification method of microseismic events based on empirical mode decomposition and artificial neural network features

Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice.

Hand gesture recognition via deep data optimization and 3D reconstruction

Hand gesture recognition (HGR) are the most significant tasks for communicating with the real-world environment. Recently, gesture recognition has been extensively utilized in diverse domains, including but not limited to virtual reality, augmented reality, health diagnosis, and robot interaction. On the other hand, accurate techniques typically utilize various modalities generated from RGB input sequences, such as optical flow which acquires the motion data in the images and videos. However, this approach impacts real-time performance due to its demand of substantial computational resources. This study aims to introduce a robust and effective approach to hand gesture recognition. We utilize two publicly available benchmark datasets. Initially, we performed preprocessing steps, including denoising, foreground extraction, and hand detection via associated component techniques. Next, hand segmentation is done to detect landmarks. Further, we utilized three multi-fused features, including geometric features, 3D point modeling and reconstruction, and angular point features. Finally, grey wolf optimization served useful features of artificial neural networks for hand gesture recognition. The experimental results have shown that the proposed HGR achieved significant recognition of 89.92% and 89.76% over IPN hand and Jester datasets, respectively.

Coupling a neural network technique with CFD simulations for predicting 2-D atmospheric dispersion analyzing wind and composition effects

The Computational Fluid Dynamics (CFD) tool has a remarkable applicability for the prediction of gas dispersion flows by numerically solving the proper governing equations in realistic scenarios. Depending on the problem complexity, undesirably high computational costs can be incurred, which has encouraged the combined use of Machine Learning (ML) seeking to attenuate the CFD simulations requirement for multiple scenario studies. The present work aims at demonstrating the employment the coupling between CFD and the Artificial Neural Network (ANN) algorithm for representative problems in atmospheric dispersion in a preliminary assessment. A limited set of CFD simulation results was used for training neural networks, whose output is given by flow field interpolators, the potential uses of which include digital twin designing and optimization procedures. One possible strategy is the local approach, which treats the network as a transition rule in the scope of Cellular Automata (CA) modeling, allowing it to learn the dynamic behavior of the addressed physics locally. This method gives rise to simpler neural network architectures with closer computing relatively to the CFD calculation. Assessments have been done by predicting, initially, a scalar field time evolution governed by a 1-D advection-diffusion transport equation to verify the method implementation. Subsequently, species concentration distributions were sought in atmospheric dispersion cases from CFD simulations datasets, comprising four case studies followed in the performed analysis, all considering a bidimensional flow domain and a scenario involving methane leaks. The first one indicated an accurate reproduction of subsequent time steps concentration field referring to the displacement of a methane cloud. The second and third cases concerned a plume formation, in transient and steady-state regimes, respectively; their main outcome was the evidence of the CA-ANN methodology's flexibility to address time-dependent and permanent flow simulations interpolation. The last CFD-based case study comprised an additional complexity feature of gas dispersion problems: the wind influence. By redesigning the investigated data-driven approach in terms of ANN's features and labels choice, promising results followed from the analysis with respect to the simultaneous capturing of two global simulation parameters (wind and leakage speeds boundary conditions) in the species concentration field interpolation.

Application of Combination Forecasting Model in Aircraft Failure Rate Forecasting.

Effective prediction of aircraft failure rate has important guiding significance for formulating reasonable maintenance plans, carrying out reliable maintenance activities, improving health management levels, and ensuring the safety of aircraft flight, etc. Firstly, combining the advantages of time series model in eliminating random accidental factors interference, grey model in dealing with poor information, and the characteristics of artificial neural network in dealing with nonlinear data, the failure rate of aircraft equipment is predicted by ARIMA model, grey Verhulst model, and BP neural network model, and secondly, based on the idea of variable weight, the method of sum of squares of errors is used to reciprocate. Shapley value method and IOWA operator method determine the weighting coefficient and establish three combined forecasting models for aircraft failure rate prediction, so as to improve the accuracy of the algorithm. Finally, taking the data of actual aircraft failure rate as the research object, the performance indexes of design prediction model are judged by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Index of Agreement (IA), Theil Inequality Coefficient (TIC), Equal Coefficient (EC), Nash-Sutcliffe Efficiency coefficient (NSE), Pearson test, and violin diagram of forecast error distribution. The experimental results show that: The forecasting precision of the combination model is better than that of the single model, and the evaluation index of combination forecasting model based on IOWA operator is better than that of other combination forecasting models, thus improving the forecasting accuracy and reliability. Compared with other typical prediction models simultaneously, it is verified that the proposed combined prediction model has strong applicability, high accuracy, and good stability, which provides a practical and effective technical method for aircraft fault prediction and has good application value.

Application of Artificial Neural Network in Chemical Separation Process

The chemical separation process is a necessary unit operation to obtain qualified purity products. The design and operation control of the separation device are very important. Due to the numerous parameters that affect the separation efficiency and product purity, it is very difficult to optimize by traditional methods. Artificial neural network (ANN) has strong fault tolerance because of its self-learning, self-organization, self-adaptive and strong nonlinear function approximation ability. ANN can be used to map the complex nonlinear relationship between dependent variables and independent variables, and can be used for the design and control of chemical separation devices. This paper summarizes the characteristics of artificial neural network and several important artificial neural network models, and discusses the application of artificial neural network in different chemical separation processes.

Predicting the Optimum Performance of a Vertical-Axis Savonius Wind Rotor With Parametric Modeling Using Artificial Neural Network and Golden Section Method

Abstract This paper explores the function approximation characteristics of Artificial Neural Network (ANN) by implementing it on the vertical-axis Savonius wind rotor technology. In this regard, a suitable experimental dataset documented in literature is exploited to train the ANN comprising the rotor performance as output and 11 different design and operating parameters as input with the help of matlab R2020b software. Multiple ANN models are trained by varying the number of hidden neurons which are then evaluated based on their estimation error and correlation coefficient (R) as decision criteria. The optimum ANN architecture demonstrates R ≈ 0.96 and 0.98 for the testing and training datasets, respectively. Further, in the quest of finding the optimum performance from the entire power curve of the rotor, the Golden Section Method (GSM) is linked with the trained ANN model. Using these soft computing techniques, a parametric study is carried out to understand the dependency of rotor performance on their design and operating parameters. At the end, a graphical interface is developed as a product so as to allow the user to predict the performance of the new rotor designs intuitively.

Performance Analysis of ANFIS-PSO based STATCOM in an Isolated Renewable Energy based Micro-Grid

In recent times, the micro-grids have gained more importance owing to the increased need for providing reliable and quality power to the users at remote locations. This work proposes a novel application of Adaptive Neuro-Fuzzy Inference System (ANFIS) in conjunction with Particle Swarm Optimization (PSO)-based controller for static synchronous compensator to improve the performance of a hybrid renewable energy based isolated micro grid environment. Adaptive and robust ANFIS controller combines the features of artificial neural network and fuzzy inference system. The proposed controller provides reactive power compensation and maintains the stability of the system. The functionality of the system is tested in MATLAB/Simulink under varying conditions and results show improvement in power factor and reduction in harmonics. Also, the performance of PSO-ANFIS is superior to conventional PI and ANFIS controller in terms error components.

Grey-Based Taguchi Multiobjective Optimization and Artificial Intelligence-Based Prediction of Dissimilar Gas Metal Arc Welding Process Performance

The quality of a welded joint is determined by key attributes such as dilution and the weld bead geometry. Achieving optimal values associated with the above-mentioned attributes of welding is a challenging task. Selecting an appropriate method to derive the parameter optimality is the key focus of this paper. This study analyzes several versatile parametric optimization and prediction models as well as uses statistical and machine learning models for further processing. Statistical methods like grey-based Taguchi optimization is used to optimize the input parameters such as welding current, wire feed rate, welding speed, and contact tip to work distance (CTWD). Advanced features of artificial neural network (ANN) and adaptive neuro-fuzzy interface system (ANFIS) models are used to predict the values of dilution and the bead geometry obtained during the welding process. The results corresponding to the initial design of the welding process are used as training and testing data for ANN and ANFIS models. The proposed methodology is validated with various experimental results outside as well as inside the initial design. From the observations, the prediction results produced by machine learning models delivered significantly high relevance with the experimental data over the regression analysis.

ПІДВИЩЕННЯ ЕФЕКТИВНОСТІ ДІАГНОСТИКИ ШИХТОВАНИХ ОСЕРДЬ ТА ВИЗНАЧЕННЯ ПОТОЧНОГО СТАНУ СИНХРОННИХ ДВИГУНІВ

Topicality. Today a large number of synchronous motors with long mean-time-between-failures operate under the conditions of industrial enterprises. They are mainly characterized by difference from the published electromagnetic parameters and degraded energy and reliability indicators, increased heating of the windings and electromagnetic vibrations, which causes a reduction in power on the shaft. Among other reasons, this is due to changes in the properties of laminated stator cores, which is the main structural element that is not replaced during repairs. Purpose. Determination of the main set of independent diagnostic parameters that would reliably distinguish the main types of defects of stator cores, first of all loosening and shorting, during their local testing using induction method and features of artificial neural networks in problems of video identification of core surface and determination of the state of synchronous motors. Metodology. The research methods of theoretical calculations of nonlinear electric circuits with non-sinusoidal power supply and methods of video identification for determination of diagnostic parameters and defects of laminated cores and the theory of neural networks in determining the current state of cores and synchronous motors in general were used during the research. Results. The obtained results specify the conditions of local testing of laminated cores from the standpoint of determining the required set of diagnostic parameters sensitive to the main types of defects, using a contactless test method, reveal the features of the use of neural network analyzers to determine the defects of the tooth zone by video identification and substantiate the principles of application of artificial neural networks in problems of predicting the states of major structural units and synchronous motors in general, due to changing properties of stator cores. Originality. The obtained results allow determining the state of the stator core and taking into account the influence of its change on the current state of synchronous motors with long or intensive operation. Practical value. The research results make it possible to assess the changes in the main parameters and characteristics of synchronous motors, determined by the actual state of the laminated stator cores and take into account their impact on energy efficiency, reliability and other operating parameters of synchronous motors. References 10, figures 7.

Spatial Mode Correction of Single Photons Using Machine Learning

AbstractSpatial modes of light constitute valuable resources for a variety of quantum technologies ranging from quantum communication and quantum imaging to remote sensing. Nevertheless, their vulnerabilities to phase distortions, induced by random media, impose significant limitations on the realistic implementation of numerous quantum‐photonic technologies. Unfortunately, this problem is exacerbated at the single‐photon level. Over the last two decades, this challenging problem has been tackled through conventional schemes that utilize optical nonlinearities, quantum correlations, and adaptive optics. In this article, the self‐learning and self‐evolving features of artificial neural networks are exploited to correct the complex spatial profile of distorted Laguerre–Gaussian modes at the single‐photon level. Furthermore, the potential of this technique is used to improve the channel capacity of an optical communication protocol that relies on structured single photons. The results have important implications for real‐time turbulence correction of structured photons and single‐photon images.

ACO-based Type 2 Diabetes Detection using Artificial Neural Networks

Background: Type 2 diabetes is one of the most common diseases among people. Early diagnosis andtreatment can reduce mortality and morbidity. So far, various solutions have been proposed to predict thistype of disease.Materials and Method: In this paper, a method for diagnosing diabetes was proposed using the AntColony Optimization (ACO) algorithm. To this end, data set properties are first reduced using artificialneural network features and then prepared for classification purpose. Finally, some components of accuracyassessment on the proposed system were calculated.Results: The simulation results show that by adjusting the parameters of ANN and ACO, about 3.2% betterprediction accuracy is obtained than other researches.Conclusion: The results of experiments represent that the proposed method is proper for health managementin diabetes.

Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis.

BackgroundTuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM.Material and MethodsFor the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machine-learning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information.ResultsThe training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machine-learning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P <0.001) and an infectious disease specialist (AUC 0.76; P = 0.03).ConclusionThe machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.

Prediction of chloride diffusivity in concrete using artificial neural network: Modelling and performance evaluation

Chloride ingression is the main reason for causing durability degradation of reinforced concrete (RC) structures. In this study, the distinguishing features of artificial neural network (ANN) technique are utilized to develop a rational and effective predictive model for chloride diffusion coefficient of concrete. An extensive and reliable database comprising of 653 distinctive diffusion coefficient results, from literature, was utilized for establishing the network model. The developed ANN models used 13 most influential parameters, varying from concrete constituents, mechanical property and experimental process, as input to incorporate complex underlying physical phenomena for prediction of diffusion coefficient. The significance of normalization of the variables is highlighted through a comparative study. The performance of the developed model is assessed by conducting several in-depth statistical error analysis as per the recommendations in literature. The results of the study reveals that the models are robust and possess a strong prediction potential. The findings revealed that ANN can be an effective tool to identify the discrepancies in the experimental findings, and would be particularly useful for evaluating the chloride resistance of RC structures serving in complex or harsh environment.

Application of artificial neural networks in detection and diagnosis of gastrointestinal and liver tumors.

As a form of artificial intelligence, artificial neural networks (ANNs) have the advantages of adaptability, parallel processing capabilities, and non-linear processing. They have been widely used in the early detection and diagnosis of tumors. In this article, we introduce the development, working principle, and characteristics of ANNs and review the research progress on the application of ANNs in the detection and diagnosis of gastrointestinal and liver tumors.

Identification of light sources using machine learning

The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation, and molecule fluorescence has relied on the collection of photon statistics or the implementation of quantum state tomography. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this article, we exploit the self-learning features of artificial neural networks and the naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. In terms of accuracy and number of measurements, the methods described here dramatically outperform conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as light detection and ranging, and microscopy.

The Use of Artificial Intelligence Systems in Law Enforcement

The paper examines the technological basis and opportunities for the use of artificial intelligence systems in law enforcement. The authors describe the investigation methods and the essence of artificial intelligence, and conduct a detailed study of approaches to the taxonomy of its systems. Artificial intelligence today does not only make it possible to solve specific tasks, but also approaches human cognition in its functions. In the present legal environment, programming and automation of crime investigation and solving are used to create information and reference systems, as well as databases and criminalistic algorithms that optimize, for example, the process of developing and verifying criminalistic leads, planning an investigation, supporting the maintenance of order, searching for the culprit, etc. The authors define key features of artificial neural networks viewed as one of the main methods of using artificial intelligence systems in law enforcement, specifically, situational adaptive learning, ability to identify non-obvious links and regularities. The designing of an applied artificial neural network is examined stage-by-stage. At the first stage, a dataset is collected - it is a volume of data for training the network. At the second stage, an algorithm (a set of rules) for learning is selected or designed. After that comes the process of learning and validating its results. The authors analyze the criteria for evaluating the effectiveness of training an artificial intelligence system, including the criteria of precision and accuracy. They single out three key types of operations in the sphere of law enforcement that can be performed by artificial intelligence systems: identification (of visual images and links between the objects of criminalistic study), prediction and classification.

Application of artificial neural networks digital filtering in measurements

The problem of this paper is suppression of the noise component of the useful signal in the researching of factors affecting the coefficient of friction of a new type of machine polymer guide rail. The parasitic component occurs during measurements, due to the vibration sensitivity of the sensor and the specifics of measurements. To suppress this component, a neural network program filter modelling the noise function has been developed. Filtering of the noise component of the signal occurs in real time, so the requirements of the software to the system resources are the determining factor, with the same accuracy characteristics of artificial neural networks. Thus, the topology of radial basis functions is optimal for the problem of modelling the physical noise function. The software implementation of the neural network filter is made in the “G” language of the LabView development environment. Approaching the number of neurons of the hidden layer to the dimension of the training sample increases the accuracy of the function simulation, but at the same time causes retraining. The accuracy of the simulation of the function of the proposed system was 0.142%.