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Inversion Research of Horizontal Well Array Induction Logging Based on Neural Network

Inversion Research of Horizontal Well Array Induction Loggin...

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Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Jie Wu Lirong Tian School of Electronic Engineering Xi'an Shiyou University Xi 'an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In order to improve the computational efficiency and accuracy of the nonlinear inversion of horizontal well array induction logging, the neural network is introduced into the inversion of induction logging data. Firstly, the database of horizontal well array induction logging samples is established. then, BP, RBF and convolutional neural network prediction models are built respectively, and training and testing are carried out based on sample database. During training, log response is taken as the input of neural network, and formation parameters are taken as the output of neural network. the prediction results of different neural network models are compared and analyzed, and the prediction results are visualized by graphs. the results show that the relative error between the inversion results of the formation parameters and the true value of the formation model is lower than 5%, and the evaluation indexes and prediction accuracy of the convolutional neural network prediction model are better than those of the BP and RBF neural network prediction models.

关键词： Training Accuracy Databases Neural networks predictive models Conductivity Data models Numerical models Arrays Convolutional neural networks

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PINN-LSTM Transformer Oil Temperature Time Series Prediction Model Based on Power Bias

PINN-LSTM Transformer Oil Temperature Time Series Prediction...

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Electrical, Electronic Information and Communication engineering (EEICE), international conference on

作者： Pengfei He Linge Li Mu Chao Jiyu Wang Xiaoqin Liu Chinese Academy of Sciences Hefei Institutes of Physical Science Hefei China University of Science and Technology of China Hefei China

ISBN: (数字)9798331532598

ISBN: (纸本)9798331532604

Accurate oil temperature prediction is critical for condition assessment of power transformers. To address the limitations of conventional models in accuracy and robustness, we propose Attention-PINN-LSTM, a hybrid framework synergizing physical thermodynamics with deep learning. the methodology features a dual-path architecture: (1) A physics-based predictor derived from discretized thermal differential equations serves as the baseline, explicitly incorporating non-uniform sampling intervals to handle irregular measurements; (2) A residual-compensation LSTM with power-biased attention dynamically corrects model deviations, where the attention mechanism prioritizes load fluctuation patterns through learnable power-weighted coefficients. Based on on-site data of operating transformers, our model achieved an R2 of 0.95 in multi-step prediction scenarios, significantly better than traditional LSTM and transformer baselines, while demonstrating consistent reliability and robustness under sudden load changes. this physics guided paradigm establishes a robust framework for modeling interpretable time series in power systems.

关键词： Attention mechanisms Oils predictive models Oil insulation Robustness Mathematical models Data models Power transformers Long short term memory Load modeling

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Multi-Criteria Optimization for Photovoltaic Power Forecasting based on CNN-BiGRU

Multi-Criteria Optimization for Photovoltaic Power Forecasti...

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international conference on Electronic engineering and Informatics (EEI)

作者： Wenshuai Lin Peitao Gao Zilong Zhang Chun Shan School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Accurate and stable prediction of PV generation has the potential to significantly enhance the stability of power systems and mitigate the reliance on conventional environmental resources. Nevertheless, many prediction models lack data processing and do not consider the model's generalisation capacity, resulting in poor robustness of the model. To solve this gap, a hybrid deep learning framework based on multi-criteria optimization and successive variational modal decomposition is presented. Firstly, the Pearson correlation analysis thermodynamic diagram is utilised to determine the input characteristics of the model, then the PV data undergoes a process of consecutive variational modal decomposition to mitigate the presence of high-frequency noise. Furthermore, a Bidirectional Gated Recurrent Unit (BiGRU) network is employed to further investigate the temporal dependencies within each subsequence. Meantime, multi-criteria particle swarm optimization is utilized to optimize the important parameter of BiGRU model. the experimental findings demonstrate that the prediction model has superior prediction accuracy compared to alternative models.

关键词： Photovoltaic systems Analytical models Accuracy predictive models Power system stability Feature extraction Prediction algorithms Data models Stability analysis Optimization

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Resilient predictive Control of Constrained Connected and Automated Vehicles under Malicious Attacks

Resilient Predictive Control of Constrained Connected and Au...

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Industrial Cyber-Physical Systems (ICPS)

作者： Henglai Wei Yan Wang Jicheng Chen Hui Zhang Centre of Excellence for Testing Research of Autonomous Vehicles NTU (CETRAN) and School of Mechanical and Aerospace Engineering Nangyang Technological University Singapore School of Mechanical and Aerospace Engineering Nangyang Technological University Singapore School of Transportation Science and Engineering Beihang University Beijing China

In this paper, we present a novel resilient distributed model predictive control (RDMPC) framework for the con-strained Connected and Automated Vehicles (CAV) in the pres-ence of $F$ -local malicious attacks. the proposed framework aims to ensure constraint satisfaction and identify malicious attacks using previously broadcast information and a convex set, referred to as the ”resilience set.“ Compared to the well-known Mean Subsequence Reduced (MSR) algorithms that require (2F + 1)-robust graphs, the proposed approach significantly reduces the required robustness level to (F + 1)-robust graph. Our simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of malicious attacks on constrained CAVs while ensuring constraint satisfaction. Overall, the proposed RDMPC framework contributes to the field of resilient platoon control for CAVs and has potential implications for improving the reliability and security of CAVs in real-world scenarios.

关键词： Uncertainty Simulation predictive models Cyber-physical systems Robustness Security Communication networks

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Beam Prediction in 5G and 6G Systems: A Deep Learning Approach

Beam Prediction in 5G and 6G Systems: A Deep Learning Approa...

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international conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Shaimaa Hassanein Omar Shouman Elias Yaacoub Abdulaziz Al-Ali Tamer Khattab Computer Science and Engineering Qatar University Doha Qatar IIPL Lab Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Exploiting beamforming techniques to direct radio waves into narrow beams targeted at users are essential in 5G and 6G to ensure high data rate requirements with low latency. However, beamforming in a dynamic environment is challenging due to the high sensitivity to shift in the receiver location. therefore, an adaptive beamforming approach is needed. In this paper, we utilize DeepSense 6G and 5G MIMO datasets for optimal beam prediction using deep learning approaches. We introduce a simple approach to transform radar input into vector embeddings in the DeepSense 6G dataset. Moreover, we address the severe minority classes problem in the 5G MIMO dataset. Finally, we conduct a thorough analysis of the state-of-the-art models. the proposed model shows significant improvements in the Top-1, Top-3, Top-5, weighted average and distance based accuracies.

关键词： 6G mobile communication Deep learning Accuracy 5G mobile communication Array signal processing Time series analysis Transforms predictive models Data models Vectors

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Towards Holistic Crime Management through Descriptive Analysis and predictive Forecasting

Towards Holistic Crime Management Through Descriptive Analys...

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Advancements in Computing (ICAC), international conference on

作者： Tinaya Wijethunge Sampath Edirisinghe Mansik Jeon Jeehyun Kim Ruchire Wijesinghe Bhagya Silva Department of Computer Engineering Faculty of Engineering Nugegoda Sri Lanka Daegu Republic of Korea Department of Electrical and Electronic Engineering Faculty of Engineering Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Malabe Sri Lanka Department of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. this article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. the forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes.

关键词： Adaptation models Accuracy Uncertainty Prevention and mitigation Computational modeling predictive models Data models Spatiotemporal phenomena Forecasting Meteorology

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Remaining Available Life Prediction Model for Power Electronic Devices Based on Temporal Neural Networks

Remaining Available Life Prediction Model for Power Electron...

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Mechatronics Technology and Intelligent Manufacturing (ICMTIM), international conference on

作者： Mingtao Wang Huanzun Zhang School of Electronic and Electrical Engineering Ningxia University YinChuan China Stony Brook Institute Anhui University Hefei China

ISBN: (数字)9798331526610

ISBN: (纸本)9798331526627

Existing research methods have not been able to accurately predict the aging failure of power electronic equipment, we propose an accuracy improvement method, the goal of this study is to predict the age group and RUL of power electronic equipment more accurately. To fill the gap of existing models, this paper proposes a remaining life prediction model based on Long Short-Term Memory (LSTM) network, which is chosen as the central element of time-series neural network because of its ability to effectively capture long-term dependencies in long-term series. Using the optimization of traditional LSTM model, we determine and propose adaptive gating mechanism improved model which dynamically adjusts learning strategy of the network by device state change, and enhances the model fitting ability for nonlinear recession process. Furthermore, we develop a multilayer LSTM network and use a hybrid loss function by integrating mean square error (MSE) with absolute error (MAE) to enhance the predictive performance of the model in different failure periods. the experimental results demonstrate that the improved LSTM model has higher accuracy and stronger robustness than the traditional LSTM and other benchmark models in the RUL prediction task of multiple power electronic devices.

关键词： Adaptation models Accuracy Adaptive systems Neural networks Time series analysis predictive models Transformers Power electronics Robustness Long short term memory

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predictive Modeling of Parkinson's Disease Using Machine Learning Techniques

Predictive Modeling of Parkinson's Disease Using Machine Lea...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Sad Shariar Helali Md. Abdul Based Md. Zubair Rahman Md. Arafat Rahman Dept of Computer Science and Engineering Dhaka International University Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Parkinson's disease (PD) is a common neurodegenerative disorder that affects millions of individuals worldwide. Early and accurate diagnosis is crucial for managing symptoms and improving patient outcomes. this research paper presents a comprehensive approach to predicting Parkinson's disease using the MDVP (Multidimensional Voice Program) dataset by machine learning models integrated with a user-friendly web application. the research uses eight machine learning methods, including Decision Tree, K-Nearest Neighbors, AdaBoost, Logistic Regression, Gradient Boosting, Random Forest, Nearest Centroid, and Naïve Bayes. Among them, Gradient Boosting and Random Forest show the best result with 100.00% accuracy and additionally, the AdaBoost and Decision Tree classifiers also demonstrated high performance, achieving 98.31% accuracy. these findings suggest the potential of these models for distinguishing between Parkinson's disease and healthy individuals. Also, the web application we developed allows users to input voice data and get a prediction about having Parkinson's disease. this tool is designed to be user-friendly, providing quick and reliable results which ultimately lead to better patient care and more efficient use of healthcare resources.

关键词： Accuracy Machine learning algorithms Parkinson's disease Medical services predictive models Nearest neighbor methods Boosting Reliability engineering Decision trees Random forests

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Bluetooth Mesh Routing Optimization Based on DDPG

Bluetooth Mesh Routing Optimization Based on DDPG

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6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zhiqin Liu School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

this article carries out multi-objective optimization for the packet loss rate and delay of the Bluetooth Mesh network. Apply GRU-based network flow prediction and DDPG's intelligent routing method, and obtain the Bluetooth Mesh network load information under the SDN architecture to construct a Mesh network including delay, using the original traffic matrix and the predicted traffic matrix as the state space, based on deep determinism Policy Gradient (DDPG) algorithm simulation to obtain optimal routing, effectively allocate routing resources and manage network traffic. Experimental results show that compared with before optimization, the routes generated by this algorithm can reduce the packet loss rate and delay of the Bluetooth Mesh network.

关键词： Mesh networks Bluetooth Signal processing algorithms Packet loss Telecommunication traffic predictive models Prediction algorithms Routing Delays Optimization

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Weather Prediction Platform Using Data Mining

Weather Prediction Platform Using Data Mining

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Computing and Informatics (ICCI), international conference on

作者： Haya K. Elsonbaty Manar A. Ibrahim Noure A. Elnakeib Eman A. Noh Haneen I. Fayez Magy Elbanhawy Ahmed T. Mazrua Ibrahim F. Moawad Omar M Elzeki Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Faculty of Engineering Faculty of Computer Science and Engineering Portsaid University Egypt New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science & Engineering Mansoura University Mansoura Egypt New Mansoura University Egypt Faculty of Computer and Information Faculty of Computer Science and Engineering Sciences Ain shams University Cairo Egypt New Mansoura University Dakhlia Egypt

this study focuses on using the Weka toolkit's machine learning algorithms to forecast weather conditions based on a dataset that includes columns for precipitation, temperature_max, temperature_min, and wind. Forecasting various weather conditions, such as drizzle, rain, sun, snow, and fog, is the goal. A thorough examination has been carried out utilizing a range of Weka algorithms, covering models with higher performance and lesser accuracy. After extensive testing, the best performing logistic algorithms meta. logitboost, meta. Iteration Classifier, ***, trees.j48, and functions. Logisticwere able to predict the weather with exceptional accuracy. these models demonstrated how well they could comprehend and predict weather conditions using the parameters from the given dataset. On the other hand, weather state predictions were relatively less accurate for models using the meta. classificationvia Regression, meta. bagging, ***, misc. input, and *** algorithms. In order to furnish stakeholders with practical and captivating insights, the outcomes were condensed and shown via user-friendly and aesthetically pleasing Google Looker dashboards. With the help of these dashboards, stakeholders may interactively examine and understand the predicted weather conditions that are based on the reliable machine learning algorithms. this research attempts to provide decision-makers with easily accessible, data-driven weather forecasts by combining these predictive algorithms with Google Looker's visualization capabilities. the dashboard system's extensive analysis and visualization are intended to improve the comprehension and use of machine learning in weather prediction, promoting well-informed decision-making across a range of industries dependent on weather-sensitive operations.

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