Neural Network Technology Research Articles

Application of Neural Network in Seismic Risk Survey

The accuracy of inversion data is determined by the reliability of linear inversion method. Therefore, the study of nonlinear seismic inversion method is a meaningful work. This paper mainly aims at the application of nonlinear neural network inversion method in Fujian Risk Survey, the following work has been accomplished: (1) The theory and algorithm of the conventional multilayer feedforward neural network and some problems in the design of the model are analyzed and summarized. Aiming at the gradient descent function search algorithm in the BP algorithm, a method for global optimization is proposed by combining simulated annealing (global search algorithm) with Conjugate gradient method (local search algorithm) , the principle and implementation of simulated annealing and conjugate gradient algorithm are analyzed in detail. (2) Based on the analysis of conventional multilayer feedforward neural networks, the model structure and basic principle of probabilistic neural networks are studied. The object function search algorithm and its realization are analyzed. The method is applied to the risk elimination prediction for the first time, and the results are satisfactory. (3) The method and principle of inversion of reservoir parameters by using various seismic attribute information and neural network technology are discussed. In order to make full use of the effective information contained in seismic signal, the method of seismic attribute extraction and optimization is studied, the principle and implementation method of attribute optimal ranking by stepwise recursive method and selecting the most important attribute combination by interactive verification method are presented.

Robot control system based on deep learning and RPA

Robots can help people complete repetitive and high-risk tasks, such as industrial production, medical care, environmental monitoring, etc. The control system of robots is the key to their ability to complete tasks, and studying robot control systems is of great significance. This article used Convolutional Neural Network (CNN) and Robotic Process Automation (RPA) technologies to optimize and train the robot control system and constructed a robot control system. This article conducts perception and decision-making experiments and execution experiments in the experimental section. According to the experimental results, it can be concluded that the average image recognition accuracy of the robot control system in perception and decision-making experiments was 94.62%. The average decision accuracy was 87.5%, and the average time efficiency was 176 seconds. During the execution of the experiment, the deviation of the motion trajectory shall not exceed 5 cm, and the oscillation amplitude shall not exceed 6°; the distance from the obstacle shall not exceed 20 cm, and the movement speed shall not exceed 0.6 m/s; the operating time shall not exceed 25 hours, and the number of faults shall not exceed 0.2 times per hour, all within the normal range. The robot control system based on Deep Learning (DL) and RPA has broad application prospects and research value, which would bring new opportunities and challenges to the development and application of robot technology.

Intelligent Power Management of Electric vehicle with onboard PV by ANN-based Model Predictive Control

This paper presents a novel configuration of Hybrid Electric Vehicle (HEV) with fuel cell as primary source and an on-board Photovoltaic (PV) array as secondary source. It incorporates an advanced Model Predictive Control (MPC) system that utilizes Artificial Neural Network (ANN) technology to control induction motor of electric vehicle. The system has three main parts: a fuel cell, an electrolyzer, and a PV array. Each part has a different job in supplying power to the vehicle. The PV array functions to supplement the fuel cell by providing power when ample irradiation is available. When the car isn't being used, the extra power from the solar panels goes to the electrolyzer. There, it turns into chemical energy, making hydrogen. This hydrogen is stored in the onboard hydrogen tank for future use. To fulfil the voltage needs of both the motor and energy sources, a quadratic bidirectional buck-boost converter (QBBC) is utilized. This converter efficiently manages the voltage output while ensuring compatibility with various energy sources. The system undergoes comprehensive analysis under different irradiance and speed conditions to evaluate its performance and efficiency. To make the solar power system more efficient and cost-effective, a Maximum Power Point Tracking (MPPT) algorithm is used. This algorithm helps get the maximum power from the PV system. An improved incremental conductance algorithm is used for this, making sure it accurately finds the best power point. The control strategy incorporates both outer voltage and inner current control, to effectively control the DC output voltage of the QBBC. The vehicle's drive system employs an indirect vector-controlled induction motor. To regulate the motor's speed effectively, an advanced ANN-MPC system is used, providing precise control and high efficiency. This paper introduces a novel approach for predictive torque control of AC machines, eliminating the need for weighting factors. An ANN estimator is used to accurately expect the motor speed, which improves the overall performance of the control system. By using MATLAB/SIMULINK to test how well the proposed Electric Vehicle (EV) setup works and make sure it's effective in real-life situations. Through this comprehensive approach, the paper contributes to advancing the development of efficient and intelligent electric vehicle systems..

Railway Signal Electronic Control System Based on BP Neural Network

Railway signal system is the core system of railway command train operation. After the installation of the system equipment is completed, a comprehensive test shall be carried out through joint debugging and joint testing, and the applicability of the system to the design requirements shall be generally evaluated according to the test results. This paper focuses on the research of railway signal electronic control system. Based on BP neural network, fault information can be quickly and accurately identified in a relatively short time, and accurate electronic signal control can be realized in a complex system environment. The final result of the study shows that the difference in delay error of railway signal transmission under BP neural network technology is small, there are some subtle differences in 5 different modes, but the impact is small, in mode 1, the target time value is 0.236s, The actual reflection time is 0.322s, it can be seen that the error is controlled within 0.2s, which is feasible. Through the analysis of delay error of railway signal electronic control system, BP neural network has certain advantages in railway signal transmission.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R2 > 0.95) and cruise phases (R2 > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SynopsisThe utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.

Computer-aided mobility solutions: Machine learning innovations to secure smart urban transportation

This study introduces a groundbreaking hybrid deep learning Intrusion Detection System (IDS) tailored to improve the security of Electric Vehicle (EV) charging processes in the context of computer-aided mobility solutions for smart urban transportation. The proposed system ingeniously integrates XGBoost and Convolutional Neural Network (CNN) technologies, leveraging their combined strengths to fortify the resilience of EV charging infrastructure. In response to the demand for transparency in decision-making, our approach incorporates Explainable AI Techniques, specifically employing Shapley Additive exPlanations (SHAP) values. This not only enables the IDS to identify anomalous behavior but also provides clear insights into the features influencing the detection process. The transparency achieved is pivotal for fostering stakeholder trust, ensuring regulatory compliance, and facilitating well-informed decision-making within the dynamic landscape of smart urban transportation. A sophisticated mechanism is implemented to enable the model to autonomously adjust and learn from evolving charging patterns over time. This adaptive capability ensures the IDS remains effective in the face of changing charging behaviors, contributing to the resilience of the overall smart urban transportation system. This research not only propels the field of cybersecurity in smart urban transportation but also establishes a robust framework for the development of intelligent systems. These systems play a critical role in securing essential processes while providing interpretable insights for stakeholders in the realm of computer-aided mobility solutions.

Automated Classification of Abdominal Ultrasound lmages of the Pancreas Based on the Spectral Representation of the Border’s Contours

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. А method for classifying the contours of the boundaries of segments of a complexly structured image is described. Тhe method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. Тhe method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. Тo classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. Тhe method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Еxperimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. Тhe purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". Тhe training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. Тhe quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Тhe use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

Research on Power Equipment Fault Diagnosis Based on Improved SVM Algorithm

Recently, Data collected from safety relays and circuit breakers has been used in fault diagnostics to identify power system components that are either failing or have tripped. A tiny percentage of heating zones may cause problems when identifying faults, although the chance of aberrant heat production in power equipment is modest. The power transformer is one of the most crucial components of any electrical system. Discovering any concealed defects in power equipment is essential to ensuring a continuous supply of electrical power for customers. The challenging characteristic of such fault diagnosis in power equipment is the greater potential for harm and a great deal of time invested in testing and training. Hence, in this research, Smart Support Vector Machine enabled Artificial Neural Network (SVM-ANN) technologies have been improved for fault diagnosis in power equipment for industry. The Genetic Algorithm (GA) process the power equipment for industrial data processing and monitoring in that fault diagnosis. The classification of strategies used and their relationships to power equipment processes to identify significant trends and research problems related to intelligent fault diagnosis systems for industry. The experimental analysis of SVM-ANN outperforms fault diagnosis using the power equipment industry in terms of performance, accuracy, prediction ratio, efficiency, and error rate.

Fusing Deep Immuno Net and Random Forest to Improve the Detection of Dengue Fever: A Computational Strategy for Prompt and Precise Diagnosis

Dengue fever is a worldwide health issue caused by the virus. Effective medical management and avoidance of severe consequences need fast and precise Dengue detection. This research uses advanced image pre-processing, deep learning methods using convolutional neural networks (CNNs), and feature selection to improve Dengue diagnosis. Our first study focuses on improving input images using cutting-edge image enhancement methods, including Adaptive Contrast Enhancement using Histogram Equalization (ACE-HE)during pre-processing. This improves visual data for processing, enabling Dengue identification. Feature extraction, using a CNN architecture optimized for Dengue detection, is our core technique. We also use current neural network designs like EfficientNet and transformers to extract subtle characteristics needed for effective diagnosis. Our approach uses computational evolutionary algorithms and neural structure searches to identify the most relevant features from the large pool of retrieved information. These characteristics are gradually fused to provide a complete depiction of Dengue's complicated patterns. Dengue detection is completed using high-performance classifiers including Random Forest variations and ensemble approaches. Our architecture is adaptable and resilient, achieving above 97.92% BCCD dataset classification accuracy. This technology represents a major advance in Dengue diagnosis and meets the worldwide requirement for rapid and accurate infectious illness identification. Our strategy uses the latest computer vision and neural network technology to produce dependable and effective tools for rapid and accurate Dengue diagnosis, addressing a crucial worldwide healthcare issue.

Application of deep learning model incorporating domain knowledge in international migration forecasting

PurposeForecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.Design/methodology/approachThis study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.FindingsThe model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R2 values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.Originality/valueThis study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

Correlative Analysis and Prediction of Physical Education Data Via Machine Learning: A Case Study on Grade Evaluation Method in University

Physical education is an important way to strengthen the physical quality of students in the university. Sport performance is an important criterion for judging the physical fitness of college students. But during the process of calculating and collecting the comprehensive score for each student, teachers will use different equipment or various grading formulas to evaluate the grade for many years. Thus, referenced score will lose their values if the measurement data for each student in different years are not unified and sometimes with the subjective factor because of the manual calculations. This article analyzes the details of physical fitness test results and the relationship with the comprehensive score. It is discussed that the comprehensive score can provide serious help for teachers to know students’ physical quality level and make reasonable teaching program to each student according to their personal radar chat. Therefore, the artificial influence is excluded, and the prediction model is designed by using the principal component analysis method and the back propagation (BP) neural network technology. The predictive model allows students to evaluate their own physical test score in advance, get a preliminary understanding of their physical fitness. Comparisons are made between the applications of this model in different years and errors are discussed to verify the accuracy. The results indicate that the comprehensive score prediction model supplies one effective approach to unify the scoring standards and improve the computation efficiency in physical education.

Integration of chatbots into foreign language learning

Innovative technologies are a part and parcel of modern education. Being integrated in the education process, neural network technologies are of particular interest now. Chatbots are used for different purposes: to gain and consolidate knowledge, to test knowledge, to study foreign languages, to organize the education process, etc. The use of generative AI contributes to education efficiency, improves skills and students’ motivation. One of the most popular and promising generative neural networks is ChatGPT. The use of this chatbot got mixed reviews, and this article examines the possibilities of using chatbots in education. We attempt to use ChatGPT as a support tool for compiling a training manual on developing listening skills for EFL (English as a foreign language) students. We analyze the advantages and disadvantages of using ChatGPT for compiling teaching materials and outline recommendations on how to use this tool efficiently. We found that, in general, the use of ChatGPT optimizes the process of preparing training materials for intermediate-level students. However, it is important to take into account a number of features such as the language register, the genre and text type, semantic breakdown of the text, the risk of neural network making an error, etc. The article concludes that the chatbot, if used correctly, can become an effective virtual assistant for an EFL teacher.

NEURAL NETWORK MODELING OF PLASMA-CHEMICAL PROCESSES OF OBTAINING NANOSYSTEMS

Plasma-chemical technology is a new area of industrial chemical technology. Chemical processes in low-temperature plasma, the regularities of reactions, and the basis of plasma-chemical technology will require computer simulation. Building adequate simulation models of plasma-chemical processes for developing nanosystems and computer simulations with them allows the development of applied research studies in this subject area. Computer intelligence technologies provide an opportunity to use non-classical approaches to building mathematical models of chemical processes. Neural network technologies make it possible to create mathematical simulation models of various processes.

Novel approach for identifying VOC emission characteristics based on mobile monitoring platform data and deep learning: Application of source apportionment in a chemical industrial park

Refined volatile organic compound (VOC) emission characteristics are crucial for accurate source apportionment in chemical industrial parks. The data from mobile monitoring platforms in chemical industrial parks contain pollution information that is not intuitively displayed, requiring further excavation. A novel approach was proposed to identify VOC emission characteristics using the class activation map (CAM) technology of convolutional neural network (CNN), which was applied on the mobile monitoring platform data (MD) derived from a typical fine chemical industrial park. It converts a large amount of monitoring data with high spatiotemporal complexity into simple and interpretable characteristic maps, effectively improving the identification effect of VOC emission characteristics, supporting more accurate source apportionment of VOC pollution around the park. Using this method, the VOC emission characteristics of eight key factories were identified. VOC source apportionment in the park was conducted for one day using a positive matrix factorization (PMF) model and seven combined factor profiles (CFPs) were calculated. Based on the identified VOC emission characteristics, the main pollution sources and their contributions to surrounding schools and residential areas were determined, revealing that one pesticide factory (named LKA) had the highest contribution ratio. The source apportionment results indicated that the impact of the chemical industrial park on the surrounding areas varied from morning to afternoon, which to some extent reflected the intermittent production methods employed for fine chemicals.

Satellite Data Processing for Hydrometeorologal Research with the Use of Neural Network Technologies: The Approaches Used at Planeta State Research Center on Space Hydrometeorology

Satellite Data Processing for Hydrometeorologal Research with the Use of Neural Network Technologies: The Approaches Used at Planeta State Research Center on Space Hydrometeorology

A Mass‐Conserving‐Perceptron for Machine‐Learning‐Based Modeling of Geoscientific Systems

AbstractAlthough decades of effort have been devoted to building Physical‐Conceptual (PC) models for predicting the time‐series evolution of geoscientific systems, recent work shows that Machine Learning (ML) based Gated Recurrent Neural Network technology can be used to develop models that are much more accurate. However, the difficulty of extracting physical understanding from ML‐based models complicates their utility for enhancing scientific knowledge regarding system structure and function. Here, we propose a physically interpretable Mass‐Conserving‐Perceptron (MCP) as a way to bridge the gap between PC‐based and ML‐based modeling approaches. The MCP exploits the inherent isomorphism between the directed graph structures underlying both PC models and GRNNs to explicitly represent the mass‐conserving nature of physical processes while enabling the functional nature of such processes to be directly learned (in an interpretable manner) from available data using off‐the‐shelf ML technology. As a proof of concept, we investigate the functional expressivity (capacity) of the MCP, explore its ability to parsimoniously represent the rainfall‐runoff (RR) dynamics of the Leaf River Basin, and demonstrate its utility for scientific hypothesis testing. To conclude, we discuss extensions of the concept to enable ML‐based physical‐conceptual representation of the coupled nature of mass‐energy‐information flows through geoscientific systems.

ОБРАБОТКА СПУТНИКОВОЙ ИНФОРМАЦИИ В ИНТЕРЕСАХ ГИДРОМЕТЕОРОЛОГИИ С ИСПОЛЬЗОВАНИЕМ НЕЙРОСЕТЕВЫХ ТЕХНОЛОГИЙ: ПОДХОДЫ НИЦ “ПЛАНЕТА”

The paper presents an experience of using artificial intelligence techniques, in particular, neural networks to solve relevant problems of hydrometeorology. The results of the investigations at the Planeta State Research Center on Space Hydrometeorology in detecting clouds and snow cover from the Himawari, Electro-L, and Meteor-M satellite data, as well as on classifying cloud types according to the AHI instrument data (Himawari-8) are reported. The findings of the work on retrieving values of total ozone and water vapor according to the infrared sensing devices are demonstrated. The work on detecting the boundaries of the ice cover and river floods from medium- and high-resolution satellite instruments, as well as the technologies for temperature and humidity sensing in the microwave spectrum are considered. The studies have shown that the use of neural network technologies provides the required accuracy of the received hydrometeorological information and high speed of processing incoming data.

Cancer Detection Using Deep Learning

Abstract: One of the most prevalent diseases is cancer. Cancer is a dangerous condition with a high death rate if it is not found and treated at an early stage. This study aims to build a precise and impartial model for detecting two types of cancers (Skin Cancer, and Oral Cancer) using Deep-Learning techniques. Machine learning and computer vision techniques, particularly Convolutional Neural Networks (CNNs), help to develop this system. Deep learning techniques can help us to detect and classify skin cancer from dermoscopy images, and oral cancer from histopathological images. Cancer will be more correctly identified with Convolutional Neural Network (CNN) technology. It increases the likelihood of curing cancer before it spreads. The advantage of using CNN architectures for cancer detection is that they can learn complex features from the images and classify them. This paper proposes a deep-learning-based approach for the diagnosis of skin cancer, and oral cancer based on predefined CNN architecture DenseNet. This model intends to improve detection and clinical decision-making. It helps healthcare to fight against cancer.

Active suspension for all-terrain vehicle with intelligent control using artificial neural networks

The automotive industry focuses on developing advanced protection and stability control systems, particularly for suspension and steering, to enhance vehicle comfort, luxury, and safety. This research presents an intelligent controller for all-terrain vehicle (ATV) suspension systems based on Artificial Neural Network (ANN) technology. The controller leverages ANN capabilities to optimize system performance. MATLAB simulations were conducted to evaluate its effectiveness under various disturbances. A comparative analysis compared the ANN regulator, classic ANFIS regulator, and passive performance in different disturbance scenarios. The simulation results demonstrate exceptional performance of the ANN-based controller in displacement reduction, speed, acceleration, and robustness. The controller effectively mitigates disturbances, enhancing overall suspension system performance. These findings highlight the advantages of employing ANN technology in ATV suspensions. This research contributes to intelligent control systems advancement in the automotive industry, specifically in ATV suspensions. The demonstrated improvements have the potential to enhance passenger comfort, vehicle stability, and safety across terrains. By implementing ANN-based controllers, automotive manufacturers can optimize suspension systems, leading to improved vehicle performance. Several indicators, including RMSE, MRE, and R2, were utilized to test and validate the models. The R2 values for the three quality parameters ranged from 0.989 to 0.999, indicating a high level of consistency in the predictions made by the ANN, a "5-12-1" structure is employed. The results of this study add to the expanding body of knowledge endorsing the efficacy of ANNs in simulating and optimizing quarter-vehicle dynamics.

Optimization of Boiler Low Nitrogen Conversion Strategy and Intelligent Phase Change Control and Thermal Management: From the Perspective of Compound Neural Network Prediction and Optical Image Analysis

ABSTRACT The optimization of boiler low nitrogen conversion technology, intelligent phase change control and thermal management are studied from the perspective of compound neural network prediction and optical image analysis. Firstly, the application concept of optical imaging technology based on compound neural network prediction technology is described in detail. Then, a prediction model of boiler NOx emission based on compound neural network is established, and the key characteristic parameters extracted are trained by using Convolutional Neural Network (CNN) technology and Long Short-Term Memory (LSTM) network as input data. Finally, based on the prediction model, a practical transformation strategy of low nitrogen boiler is created, and the model is comprehensively evaluated. This strategy aims to control nitrogen oxides under the minimum emission concentration limit. The results show that the average failure rate of other traditional models is 40% and the lowest failure rate is 20%, while the highest and lowest failure rates of the model developed in this paper are about 11% and 6% respectively. Compared with the established technical methods, the research presented has a significant improvement in performance. In addition to promoting the development of environmental governance, this paper also provides support for the optimization of energy management technology.