Intelligence Learning Methods Research Articles

Emerging Trends In Artificial Intelligence Learning Methods: Deep Learning And Machine Learning Innovation In Computer Science

Emerging Trends In Artificial Intelligence Learning Methods: Deep Learning And Machine Learning Innovation In Computer Science

Fault analysis in clustered microgrids utilizing SVM-CNN and differential protection

The integration of distributed generation, microgrids, and renewable energy sources has significantly enhanced the resilience of modern electrical grids. However, this transition presents challenges in control, stability, safety, and protection due to low fault currents from renewables. This paper addresses these challenges by proposing novel methodologies to enhance fault detection, classification, and localization in microgrids. The literature review highlights a shift towards intelligent learning methods in microgrid protection systems, improving fault response times and identifying electrical faults, including high impedance faults. Nonetheless, existing methods often neglect high impedance fault detection and the integration of differential protection in clustered microgrids. To fill these gaps, this study presents a methodology combining support vector machines and convolutional neural networks for fault detection in microgrids, integrating differential protection for high impedance fault detection. The paper also proposes approaches to optimize protection in clustered microgrid systems. The effectiveness of the methodology is validated using Opal-RT through comparative analyses of signal decomposition techniques, performance and accuracy of support vector machines and convolutional neural networks, K-Fold validation, and sensitivity analysis. Results demonstrate robustness and high performance, achieving up to 100 % accuracy in fault detection and classification.

Intelligent learning methods for improving student performance through virtual reality technology

With the rapid development of information technology, the field of education is undergoing a profound change, in which intelligent hybrid learning and virtual reality technology are increasingly valued. This study proposes an intelligent hybrid learning method based on virtual reality for student performance improvement. This paper reveals the limitations of traditional learning methods in meeting the needs of modern education, and expounds the theoretical basis of intelligent hybrid learning and virtual reality technology. This paper collects and processes a large amount of learning data, based on which a new model of student learning performance prediction is established. The verification results of the model show that the model in this study has excellent performance in predicting students’ learning performance. This paper gives some suggestions for future educational practice and research. In general, this study provides a new learning method for the field of education and has important reference value for education reform and teaching practice.

Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves

Intelligent Learning Method for Capacity Estimation of Lithium-Ion Batteries Based on Partial Charging Curves

An adaptable and personalized framework for top-N course recommendations in online learning

In recent years, the proliferation of Massive Open Online Courses (MOOC) platforms on a global scale has been remarkable. Learners can now meet their learning demands with the help of MOOC. However, learners might not understand the course material well if they have access to a lot of information due to their inadequate expertise and cognitive ability. Personalized Recommender Systems (RSs), a cutting-edge technology, can assist in addressing this issue. It greatly increases resource acquisition through personalized availability for various people of all ages. Intelligent learning methods, such as machine learning and Reinforcement Learning (RL) can be used in RS challenges. However, machine learning needs supervised data and classical RL is not suitable for multi-task recommendations in online learning platforms. To address these challenges, the proposed framework integrates a Deep Reinforcement Learning (DRL) and multi-agent approach. This adaptive system personalizes the learning experience by considering key factors such as learner sentiments, learning style, preferences, competency, and adaptive difficulty levels. We formulate the interactive RS problem using a DRL-based Actor-Critic model named DRR, treating recommendations as a sequential decision-making process. The DRR enables the system to provide top-N course recommendations and personalized learning paths, enriching the student's experience. Extensive experiments on a MOOC dataset such as the 100 K Coursera course review validate the proposed DRR model, demonstrating its superiority over baseline models in major evaluation metrics for long-term recommendations. The outcomes of this research contribute to the field of e-learning technology, guiding the design and implementation of course RSs, to facilitate personalized and relevant recommendations for online learning students.

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning.

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems.

Music teaching software development based on neural network algorithm and user analysis

With the continuous development of digital technology, music teaching is gradually shifting from traditional face-to-face teaching to online teaching and intelligent learning methods. Developing music teaching software plays an important role in improving users’ music learning methods and learning experience. However, current music teaching software still has issues such as poor compatibility, low level of data security and privacy protection, and functional requirements that are still in the early stages. This article aimed to research and develop a music teaching software based on neural network algorithm and user analysis, so as to improve user satisfaction with music teaching software. In the article, the overall design module of the system was first analyzed, and then music teaching resource data was collected and normalized using clustering algorithms to improve the effectiveness of music teaching resource data. Afterwards, a BP (Back Propagation) neural network model with parallel structure was used to optimize the design of the neural network model, and the effectiveness of the neural network model in user interface design was analyzed. Finally, user behavior analysis and personalized recommendation were achieved by constructing a user profile feature dataset. In order to verify the performance of the developed music teaching software, the software performance and security were tested. The results showed that the required loading time for the music score data in test case 1 of this article was only 1.21 s, and the response time was only 0.015 s. At this time, the security was 92.6%; the required loading time for audio data was only 1.06 s. The response time was only 0.019 s, and the security was 88.7%; the loading time required for user interaction data was only 0.78 s. The response time was only 0.012 s, and the safety was 91.3%. The research results indicated that the music teaching software developed in this article could better meet users’ music teaching needs and provide users with a more intelligent, personalized, and efficient music learning experience.

VGNet: A Lightweight Intelligent Learning Method for Corn Diseases Recognition

The automatic recognition of crop diseases based on visual perception algorithms is one of the important research directions in the current prevention and control of crop diseases. However, there are two issues to be addressed in corn disease identification: (1) A lack of multicategory corn disease image datasets that can be used for disease recognition model training. (2) The existing methods for identifying corn diseases have difficulty satisfying the dual requirements of disease recognition speed and accuracy in actual corn planting scenarios. Therefore, a corn diseases recognition system based on pretrained VGG16 is investigated and devised, termed as VGNet, which consists of batch normalization (BN), global average pooling (GAP) and L2 normalization. The performance of the proposed method is improved by using transfer learning for the task of corn disease classification. Experiment results show that the Adam optimizer is more suitable for crop disease recognition than the stochastic gradient descent (SGD) algorithm. When the learning rate is 0.001, the model performance reaches a highest accuracy of 98.3% and a lowest loss of 0.035. After data augmentation, the precision of nine corn diseases is between 98.1% and 100%, and the recall value ranges from 98.6% to 100%. What is more, the designed lightweight VGNet only occupies 79.5 MB of space, and the testing time for 230 images is 75.21 s, which demonstrates better transferability and accuracy in crop disease image recognition.

Global landslide susceptibility prediction based on the automated machine learning (AutoML) framework

Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an AutoML-based global LSP framework at two spatial resolutions of 90 m and 1000 m, and achieved an area under the receiver operating characteristic above 0.96. The global prediction results were then validated using additional regional landslide inventories, including three countries, three provinces, and two prefecture-level datasets. Moreover, the global prediction results of 90 m are used to improve the performance of regional LSP. Specifically, the low-and very low-prone areas in the global prediction results were used as non-landslide samples for susceptibility modeling. Results demonstrated that the model achieved a better performance than original global prediction results. We believe that this study will be able to reliably promote the application of intelligent learning methods in global LSP.

A dynamic learning method based on the Gaussian process for tunnel boring machine intelligent driving

Introduction: The application of intelligent learning methods to the mining of characteristics and rules of time-series data has gained increasing attention with the rapid development of deep learning. One critical application of such methods is the intelligent assistant driving of tunnel boring machines (TBMs), for which the optimization of driving parameters is essential to improve construction efficiency. However, existing prediction models for TBM parameters are “static” and cannot dynamically capture parameter evolution during real-time driving cycles.Methods: In this study, we propose a novel dynamic learning model for TBM parameters by introducing the Gaussian process to address this problem. The model can learn decision-making experiences from historical driving cycles, dynamically update the model based on small sample data from current driving cycles, and simultaneously achieve driving parameter prediction. We focused on real-time prediction of TBM parameters in a tunnel project in western China.Results: The results show that the average relative errors of predicted total thrust and torque values were 1.9% and 2.7%, respectively, and the prediction accuracy was higher than that of conventional models such as random forest and long short-term memory. The model fully exploited updating of small samples of parameters, reducing the average time cost of the model to 29.7 s, which satisfies the requirements of efficient application.Discussion: The dynamic learning strategy of time-series data adopted in this study provides a reference for other similar engineering applications. The proposed model can improve the prediction accuracy of TBM parameters, thus facilitating the optimization of driving parameters and enhancing the construction efficiency of tunnels.Conclusion: In summary, this study establishes a dynamic learning model of TBM parameters that can dynamically capture parameter evolution and achieve accurate real-time driving parameter prediction. The proposed model can contribute to the development of intelligent assistant driving of TBMs and similar engineering applications.

Characterization of Flow Behaviors by a PSO-BP Integrated Model for a Medium Carbon Alloy Steel.

In order to characterize the flow behaviors of SAE 5137H steel, isothermal compression tests at the temperatures of 1123 K, 1213 K, 1303 K, 1393 K, and 1483 K, and the strain rates of 0.01 s-1, 0.1 s-1, 1 s-1, and 10 s-1 were performed using a Gleeble 3500 thermo-mechanical simulator. The analysis results of true stress-strain curves show that the flow stress decreases with temperature increasing and strain rate decreasing. In order to accurately and efficiently characterize the complex flow behaviors, the intelligent learning method backpropagation-artificial neural network (BP-ANN) was combined with the particle swarm optimization (PSO), namely, the PSO-BP integrated model. Detailed comparisons of the semi-physical model with improved Arrhenius-Type, BP-ANN, and PSO-BP integrated model for the flow behaviors of SAE 5137H steel in terms of generative ability, predictive ability, and modeling efficiency were presented. The comparison results show that the PSO-BP integrated model has the best comprehensive ability, BP-ANN is the second, and semi-physical model with improved Arrhenius-Type is the lowest. It indicates that the PSO-BP integrated model can accurately describe the flow behaviors of SAE 5137H steel.

IMPROVING HUMAN-ROBOT PHYSICAL INTERACTION COMFORT IN MATERIAL HANDLING TASKS USING A SMART PLATFORM

The use of mobile platforms can help employees automate manual processes and streamline operations to save time and perform their tasks safely and accurately. A power-assisted vehicle to move weight around the place – solution: inexpensive, easy to apply, reliable, safe. It can adjust to various tasks, operators’ gait, loads up to 500 kg. It is a relatively inexpensive, easy-to-apply, reliable, and safe solution for moving weight. The motivation of the study is to increase efficiency and reduce physical strain on the operator in material handling tasks and to promote the implementation of this smart platform. Artificial intelligence learning methods are applied to adapt to individual operator’s experience, resulting in a personalized and more comfortable interaction with the help of Q-learning algorithm with 256 learning outcomes in adjusting controller settings: damping, mass, stiffness.

A Computational Intelligence Hybrid Algorithm Based on Population Evolutionary and Neural Network Learning for the Crude Oil Spot Price Prediction

This research attempts to reinforce the cultivating expression of radial basis function neural network (RBFnet) through computational intelligence (CI) and swarm intelligence (SI) learning methods. Consequently, the artificial immune system (AIS) and ant colony optimization (ACO) approaches are utilized to cultivate RBFnet for function approximation issue. The proposed hybridization of AIS and ACO approaches optimization (HIAO) algorithm combines the complementarity of exploitation and exploration to realize problem solving. It allows the solution domain having the advantages of intensification and diversification, which further avoids the situation of immature convergence. In addition, the empirical achievements have confirmed that the HIAO algorithm not only obtained the best accurate function approximation for theoretically standard nonlinear problems, it can be further applied on the instance solving for practical crude oil spot price prediction.

AI-Based Predictive Modelling of the Onset and Progression of Dementia

Dementia, the most severe expression of cognitive impairment, is among the main causes of disability in older adults and currently affects over 55 million individuals. Dementia prevention is a global public health priority, and recent studies have shown that dementia risk can be reduced through non-pharmacological interventions targeting different lifestyle areas. The FINnish GERiatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) has shown a positive effect on cognition in older adults at risk of dementia through a 2-year multidomain intervention targeting lifestyle and vascular risk factors. The LETHE project builds on these findings and will provide a digital-enabled FINGER intervention model for delaying or preventing the onset of cognitive decline. An individualised ICT-based multidomain, preventive lifestyle intervention program will be implemented utilising behaviour and intervention data through passive and active data collection. Artificial intelligence and machine learning methods will be used for data-driven risk factor prediction models. An initial model based on large multinational datasets will be validated and integrated into an 18-month trial integrating digital biomarkers to further improve the model. Furthermore, the LETHE project will investigate the concept of federated learning to, on the one hand, protect the privacy of the health and behaviour data and, on the other hand, to provide the opportunity to enhance the data model easily by integrating additional clinical centres.

Adaptive Differential Evolution Algorithm with Simulated Annealing for Security of IoT Ecosystems

With the wide application of the Internet of Things (IoT) in real world, the impact of the security on its development is becoming incrementally important. Recently, many advanced technologies, such as artificial intelligence (AI), computational intelligence (CI), and deep learning method, have been applied in different security applications. In intrusion detection system (IDS) of IoT, this paper developed an adaptive differential evolution based on simulated annealing algorithm (ASADE) to deal with the feature selection problems. The mutation, crossover, and selection processes of the self-adaptive DE algorithm are modified to avoid trapping in the local optimal solution. In the mutation process, the mutation factor is changed based on the hyperbolic tangent function curve. A linear function with generation is incorporated into the crossover operation to control the crossover factor. In the selection process, this paper adopts the Metropolis criterion of the SA algorithm to accept poor solution as optimal solution. To test the performance of the proposed algorithm, numerical experiments were performed on 29 benchmark functions from the CEC2017 and six typical benchmark functions. The experimental results indicate that the proposed algorithm is superior to the other four algorithms.

Composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism

This paper focus on the novel composite neural learning event-triggered control for dynamic positioning vehicles with the fault compensation mechanism. In the proposed algorithm, the system uncertainties are tackled with by incorporating the dynamic surface control (DSC) and the robust neural damping techniques. Besides, the serial–parallel estimation model (SPEM) is developed to produce the prediction errors, which could be employed to derive the corresponding composite adaptive law. And the unknown actuator faults and gain uncertainties are effectively compensated for merits of the composite intelligent learning method. Furthermore, the idea of relative threshold strategy is utilized to construct the event-triggered mechanism. The event-triggered input could reduce the communication burden in the channel from controller to actuators. Through the direct Lyapunov theory, the parameters setting can be derived to guarantee the semi-global uniformly ultimately bounded (SGUUB) stability of all error signals in the closed-loop system. Finally, the effectiveness of the proposed algorithm is validated through the simulation experiments.

A Machine Learning Based Intelligent Decision Support System for Flotation Dynamics in Simulation of Clay Minerals

In coal mines, process management is related with planning and flow of coal minerals from their mining points to journey’s end. The process is dependent on the operational decisions, which should be completed during coal production. In these systems, simulation modelling is assumed a powerful tool for decision making. The simulation modelling can further be enhanced through applying artificial intelligence (AI) and machine learning (ML) methods. The communication of water with clay minerals upholds the water adsorption on the clay surface, which makes them complex systems. Therefore, evading the water absorption from the clay turns out to be a hard job. The computational trainings of clay minerals are needed to comprehend the dynamics of water distribution. Tradition of coal slurry treatment is completed by adding medicament of its mineral flocculation sedimentation. As a result of coal, slime water contains a lot of clay minerals that are rich in kaolinite versatility and it is difficult to settle. The flotation will be one of the kaolinite recycling. In this paper, clay minerals containing a variety of minerals were taken as samples, and sodium dodecyl sulfate and sodium oleate were used as collectors to explore the flotation effect through test and molecular dynamics simulation. A machine learning based intelligent decision support system is designed to improve the outputs of the simulation model. The results show that when the pH value is 8 and the amount of collector and sec-octanol are 150 g/t and 250 g/t, respectively, the flotation rate of fine mineral can reach 63.25%. According to the molecular dynamics simulation results, the addition of the collector can reduce the hydrophilicity of the kaolinite surface, and the physical adsorption of SDS only occurs on the (001) surface.

Data Transmission Reliability Analysis of Wireless Sensor Networks for Social Network Optimization

With the rapid development of the Internet in recent years, people are using the Internet less and less frequently. People publish and obtain information through various channels on the Internet, and online social networks have become one of the most important channels. Many nodes in social networks and frequent interactions between nodes create great difficulties for privacy protection, and some of the existing studies also have problems such as cumbersome computational steps and low efficiency. In this paper, we take the complex environment of social networks as the research background and focus on the key issues of mobile wireless sensor network reliability from the mobile wireless sensor networks that apply to large-scale, simpler information, and delay tolerance. By introducing intelligent learning methods and swarm intelligence bionic optimization algorithms, we address reliability issues such as mobile wireless sensor network fault prediction methods and topology reliability assessment methods in industrial application environments, the impact of mobile path optimization of mobile wireless sensor networks on data collection efficiency and network reliability, reliable data transmission based on data fusion methods, and intelligent fault tolerance strategies for multipath routing to ensure mobile wireless sensor networks operate energy-efficiently and reliably in complex industrial application environments.

Intelligent Design of Robotic Welding Process Parameters Using Learning-Based Methods

With the wide application of multi-layer and multi-pass welding in industry, the traditional manual welding method is difficult to meet the needs of manufacture. Welding Robot has the advantages of stable productivity, ensuring welding quality even in special environment, so the welding robots are used at a growing trend in manufacturing fields to complete different welding tasks. In this paper, an intelligence learning method for welding robot is designed, aiming at the prediction of welding process parameters and bead geometry parameters in the welding process, deep and machine learning algorithms are used for realization. It provides an instruction for the design of process parameters to realize the intellectualization and automation of welding robot. The experimental results show that automatic parameters learning based on machine learning are effective and different learning methods should be selected for different process parameter prediction tasks in order to achieve the best prediction effect.

A comparative analysis of Statistical and Computational Intelligence methodologies for the prediction of traffic-induced fine particulate matter and NO2

With the urbanization increase, urban mobility and transportation induce higher traffic volumes causing environmental, economic and social impacts. This is due to continuous usage of fossil fuel energy resources generating air pollutants, such as nitrogen oxides (NOx), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3) and particulate matter (PM10 and PM2.5), which impact on climate and air quality and adversely affect the human health. The present paper aims at training an ensemble of forecasting methodologies for traffic-induced pollutant emissions and implementing it for predicting PM10, PM2.5 and NO2 for the case study of Cambridge, UK inner-city region. Such an ensemble enables decision makers to evaluate the impact of various transportation policies and measures on human health and the ecosystem, and subsequently contribute towards urban resilience and sustainability. Since the chemical synthesis of air pollution is triggered by meteorological factors, the forecasting incorporates them along with the traffic volumes. We opted to combine Statistical and Computational Intelligence learning methods including Adaptive Neuro Fuzzy Inference Systems (ANFIS), Long Short-Term Memory (LSTM) recurrent neural networks and Extreme Learning Machines (ELM). Initially, Multivariate Imputation by Chained Equation (MICE) and trend and seasonality removal was performed at data preprocessing and then Principal Component Analysis (PCA) highlighted the principal parameters for ANFIS to predict next day's PM10, PM2.5 and NO2 values. LSTM and ELM methods estimated next day values and compared with the ANFIS model results for hourly time series data of length 2703. The performance of the embedded models was quantified by the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), and Coefficient of Determination (R2) indices. The ensemble was found to be superior in predicting PM10, PM2.5 and NO2 emissions when compared with existing traditional models.