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检索条件"任意字段=Conference on Data Analysis and Modeling for Process Control II"

共 4775 条记录，以下是951-960 订阅

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Analyzing the performance of supervised and unsupervised learning for satellite image classification: A case study of Casablanca

Analyzing the performance of supervised and unsupervised lea...

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data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International conference on

作者： Hafsa Ouchra Abdessamad Belangour Allae Erraissi Laboratory of Information Technology and Modeling LTIM Hassan II University Faculty of Sciences Ben M'sik Casablanca Morocco Polydisciplinary Faculty of Sidi Bennour Chouaib Doukkali University El Jadida Morocco

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

Satellite image classification plays a crucial role in various applications such as urban planning, environmental monitoring, and land use analysis. In this study, we present a comparative analysis of different supervised and unsupervised learning methods for satellite image classification, focusing on a case study in Casablanca using Landsat 8 imagery. The aim of this research is to identify the most effective machine-learning approach for accurately classifying land cover in an urban environment. The study begins with a comprehensive review of satellite image classification techniques and machine learning methods commonly used in this field. We explore the strengths and limitations of each method used, such as Random Forest, Cart and Support Vector Machines (SVMs), etc. Using Landsat imagery data from Casablanca, we pre-process the data, extract relevant features, and partition them into training and test sets. Through a series of experiments, we evaluate the performance of each machine learning method in terms of accuracy, and Kappa coefficient. Our results highlight the varying abilities of these methods to accurately classify different land-use categories in an urban context. We discuss the factors that influence their performance, including data characteristics, feature selection, and model complexity.

关键词： Training Urban planning Software algorithms Land surface Landsat Feature extraction Satellite images

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Selective Edge Shedding in Large Graphs Under Resource Constraints 37

Selective Edge Shedding in Large Graphs Under Resource Const...

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37th IEEE International conference on data Engineering (IEEE ICDE)

作者： Zeng, Yiling Song, Chunyao Ge, Tingjian Nankai Univ Coll Comp Sci Tianjin Key Lab Network & Data Sci Technol Tianjin Peoples R China Univ Massachusetts Lowell Lowell MA USA Nanjing Univ Posts & Telecommun Jiangsu Key Lab Big Data Secur & Intelligent Proc Nanjing Peoples R China

ISBN: (纸本)9781728191843

With the rapid development of the information age, many complex systems can be modeled as graphs. However, the unprecedented growth of data makes it extremely difficult for everyday users to process and mine very large graphs, given their limited computing resources such as personal computers and laptops. To address this challenge, we propose selective edge shedding. By estimating the original graph information from the reduced graph, it can accelerate graph algorithms and queries. In this paper, we propose two vertex-degree preserving edge shedding methods, the core of which are to maintain the expected vertex degree, so as to capture the basic characteristics of the network. Both methods allow users to control the size of the reduced graph based on the computing resource constraint. The experimental results show that the methods proposed in this paper can achieve up to 65% higher accuracy on graph analysis tasks compared to the competitive method, while consuming only 26 %-57 % running time, which fully demonstrates the advantages of the methods proposed in this work.

关键词： Portable computers conferences Computational modeling Information age data engineering controllability Task analysis

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A Study on Weed Recognition Based on an Improved YOLOv8 Model

A Study on Weed Recognition Based on an Improved YOLOv8 Mode...

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Robotics, Artificial Intelligence and Intelligent control (RAiiC), International conference on

作者： Jiankun Liu Mechanical college Shanghai Dianji University Shanghai China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

To advance the application of artificial intelligence in complex environments, this study focuses on weed recognition in spinach fields and proposes an intelligent recognition method based on an improved YOLOv8 model. The process begins with image preprocessing and data augmentation to enhance the dataset. Then, the Large Separable Kernel Attention (LSKA) mechanism is introduced to improve target recognition in complex backgrounds. To simplify the network structure and reduce computational demands, the Deformable Convolutional Network (DCN) is employed. The original loss function is replaced with the Minimum Point Distance IoU loss function to accelerate convergence. Comparing the performance of several popular object detection models, we find experimental results demonstrated that the improved model achieved excellent performance, with accuracy, recall, and average precision reaching $83.4 \%, 70.2 \%$, and $73.6 \%$, respectively. The model’s performance across various configurations confirmed the effectiveness of the proposed improvements in enhancing response speed and handling complex scenes. The comprehensive application of these strategies improved various metrics while maintaining an acceptable increase in model complexity. This study provides a new technical method and theoretical foundation for applying artificial intelligence in real-time, efficient target recognition.

关键词： Adaptation models Attention mechanisms Target recognition Image color analysis Convolution Computational modeling Object detection

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A Naive Bayes Classifier for Fault Detection and Classification Using Dimension Reduction Technique

A Naive Bayes Classifier for Fault Detection and Classificat...

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Electronic Engineering, Information Technology & Education (EEITE), International conference in

作者： Nasar Aldian Ambark Shashoa Omar Abusaeeda Omer S.M. Jomah Musa Faneer Department of Electrical and Computer Engineering Libyan Academy for Postgraduate Studies Tripoli Libya Department of Computer Networks University of Tripoli Tripoli Libya

ISBN: (数字)9798350372878

ISBN: (纸本)9798350372885

Fault detection and classification is critical to the reliability of modern control systems in different industries, where detecting and classifying faults in operational processes are very important things while failure to detect and classify them, may cause irreparable damage. In this paper, fault detection and classification approach is presented. The first step, multi stage recursive least squares parameter estimation approach for controlled autoregressive autoregressive moving average systems (CARARMA) is developed with a view to estimate the parameters of the system, additionally, improve the effectiveness of the computation. By means of multi stage approach, the (CARARMA) system is decomposed into three simple identification models, and the parameters of each simple model is identified one by one. These parameters estimated by this approach are referred to as features, and not all of them have the same useful data about the system. In order to select the valuable features and improve a classification accuracy, the Linear Discriminant analysis (LDA) approach based on scattering matrices is applied for dimension reduction. The classification between these reduced classes is done based on the Naive Bayes classifier. Finally, the obtained results explain the performance of this proposed approach.

关键词： Dimensionality reduction Parameter estimation Fault detection Computational modeling Scattering process control Naive Bayes methods

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Optimizing Battery Performance in Electric Vehicles Through Predictive modeling

Optimizing Battery Performance in Electric Vehicles Through ...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Kumar, C. Siva Sagar, P. Lakshmi Venkata Bhanu Pratap, B. Geethamadhuri, Gurramkonda Kumar, Banda Sai Ram Dinesh, Bommineni Mukesh, Pallaputhimmannagari Sree Vidyanikethan Engineering College Dept of Csse Andhra Pradesh Tirupati India Dept of Data Science Karakambadi A.P Tirupati India BORiTS Private Limited Telangana Hyderabad India Ug School Dept of Csbs Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India

ISBN: (纸本)9798331527495

The fast-paced electrification of the automobile sector has intensified an immediate need for reliable battery management systems capable of sustaining long-term performance. One of the most important indicators when it comes to battery health is State of Health (SOH), which indicates how much capacity is left and how long a certain battery will endure. Reliable estimation of SOH is critical to enable the optimal performance of vehicles, reduce service costs, avoid unanticipated failures. In this project, we propose a machine learning approach to predict the SOH of EV batteries based on the two widely used algorithms, XGBoost and LGBM. In order to cover and analyze as many process variables available as possible to build a database for the training and evaluation of the models, we measured multiple battery parameters, such as voltage, current, temperature and historical charge/discharge cycles. The train test split this dataset in such a way that the greater part of the dataset is used for the machine learning operations. Individual models, such as XGBoost and LGBM, are used to be train due to its strength in handling large data and complex data with a high precision. This study attempts to find out the better of the two algorithms for real-time SOH prediction in practice by analyzing the accuracy and performance metrics of both algorithms. Results of this analysis can help manufacturers and operators make data-informed decisions regarding battery usage, maintenance schedules and replacement, which will contribute to improved vehicle reliability and extended battery life. © 2025 IEEE.

关键词： Decision making

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Optimizing Battery Performance in Electric Vehicles Through Predictive modeling

Optimizing Battery Performance in Electric Vehicles Through ...

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Intelligent control, Computing and Communications (IC3), International conference on

作者： C Siva Kumar P. Lakshmi Sagar Venkata Bhanu Pratap B Gurramkonda Geethamadhuri Banda Sai ram Kumar Bommineni Dinesh Pallaputhimmannagari Mukesh Dept of CSSE Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh India Dept of Data Science Sri Venkateswara College of Engineering (Autonomous) Karakambadi Tirupati A.P India BORiTS Private Limited Hyderabad Telangana India UG School Dept of CSBS Sree Vidyanikethan Engineering College Tirupati Andhra Pradesh India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

关键词： Temperature measurement Schedules Accuracy Machine learning algorithms Voltage measurement Predictive models Prediction algorithms Electric vehicles Real-time systems Batteries

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Construction of Real Mesostructure Model and Numerical analysis of Uniaxial Compression for Fiber Reinforced Permeable Concrete

Construction of Real Mesostructure Model and Numerical Analy...

引用

Industrial IoT, Big data and Supply Chain (iioTBDSC), International conference on

作者： Fusheng Wen Wenchao Li Guangfa Zhou Fushan Ma Kesen Huang Kunqiang Zhang College of Mechanical and Architectural Engineering Taishan University Tai'an China Yangtze River Water Conservancy and Hydropower Engineering Construction (Wuhan) Co. Ltd. Jinan China No.3 Engineering Co. Ltd. of CCCC First Highway Engineering Co. Ltd Beijing China College of Water Conservancy and Civil Engineering Shandong Agricultural University Tai'an China

ISBN: (数字)9798350341690

ISBN: (纸本)9798350341706

In this paper, fiber reinforced permeable concrete (FRPC) is considered to consist of aggregate, mortar (containing polyacrylonitrile fibers inside), interface transition zone (ITZ) and pores from mesoscopic perspective. Trainable Weka Segmentation (TWS) segmentation algorithm and morphological processing were applied on Computer Tomography (CT) images to extract the components of FRPC to establish a 3D real mesostructure model with realistic spatial structure. The 3D model was used to carry out qualitative numerical simulation of uniaxial compression process of FRPC specimen. Stress, displacement, macroscopic and mesoscopic cracks of the model were analyzed to reveal the fracture process of FRPC. The results showed that the fracture of the model is mainly caused by stress concentration and large deformation at local weak positions. The macroscopic fracturing process of the model consists of three stages: (i) crack initiation period, (ii) slow development period, (iii) rapid propagation period model fracture period. The fracturing process in mesoscopic componnets can be concluded as a progressive failure that consists of three stages: (i) pores and ITZ cracking period, (ii) cement mortar cracking period, (iii) aggregates fracture and structural failure period. The accuracy and reliability of the numerical model construction method was verified by comparing the crack distribution pattern of the model and the destruction characteristics of the specimen in physical uniaxial compression experiment.

关键词： Deformable models Solid modeling Image coding Three-dimensional displays Accuracy Mortar Aggregates

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Electric Vehicle Charging Demand Prediction using Machine Learning Algorithms

Electric Vehicle Charging Demand Prediction using Machine Le...

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International conference on Inventive Systems and control (ICISC)

作者： K N B Sai Hitesh P Saran Teja J Ram Kumar Computing Technologies SRM Institute of Science and Technology CHENNAI INDIA

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

The increasing concerns regarding climate change, notably the surge in greenhouse gas emissions, have attained paramount significance in the agendas of both U.S. and global policy makers. Transportation, responsible for $29 \%$ of U.S. greenhouse gas emissions according to the U.S. Environmental Protection Agency (EPA), stands as the nation’s leading contributor to this challenge. In parallel, substantial policy initiatives at the national and state levels are underway, and key automotive manufacturers like Ford, GM, and Volvo have committed to a complete transition to electric vehicles (EVs) within the next decade, heralding a profound shift in the automotive industry. This impending transformation, combined with public and private sector efforts, is poised to generate a substantial increase in EV demand. Governments and public utilities can use this research to build and manage public charging networks strategically by applying machine learning models to forecast energy consumption patterns inside EV charging infrastructure. By proactively forecasting future EV charger energy consumption, this study aims to pinpoint regions of high demand, facilitating optimized infrastructure expansion and resource utilization, aligning with sustainability goals and the evolving EV landscape. We harnessed a real-time dataset comprising 433,172 electric vehicle transactions, including 91,255 new vehicle registrations. We integrated data from the National Renewable Energy Laboratory’s API, encompassing 1,686 charging stations in Washington. Our modeling process, applied uniformly across counties, included seasonal decomposition, training and testing data partitioning, gridsearch with pmdarima for parameter optimization, and SARIMAX modeling for forecasting. Visualization aided in assessing forecast accuracy, contributing to our overall study on EV charging demand prediction.

关键词： Climate change Electric vehicles Electric vehicle charging Machine learning Time series analysis Greenhouse gases Supply and demand Government policies

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AlcoNet: A Robust Deep Learning-based Framework for Detecting Alcoholism using EEG

AlcoNet: A Robust Deep Learning-based Framework for Detectin...

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Intelligent Technologies (ACOIT), Asian conference on

作者： Sayan Mondal Debstuti Biswas Prithwijit Mukherjee Anisha Halder Roy Institute of Radio Physics and Electronics University of Calcutta Kolkata India Electrical and Computer Engineering University of Arizona Tucson AZ United States

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

Alcoholism is a profound disorder that represents a substantial concern for present-day society. If alcoholism is not diagnosed at an early stage, then it negatively impacts a patient’s life. Patients affected by alcoholism can face a variety of health complications, including kidney damage, cardiovascular issues, neurological and mental disorders, behavioural and social problems, digestive issues, immune system suppression, cancer risk, reproductive problems, and even death. Currently, there are no standard procedures for diagnosing alcoholism. The electroencephalogram (EEG) signal of the human brain has proven to be useful for diagnosing alcoholism. In this research, a deep learning-based methodology is adopted for classifying a subject into one of two classes, alcoholic or non-alcoholic, by analyzing EEG signals. A publicly available dataset containing EEG signals from both alcoholic and non-alcoholic (control) subjects is utilized for the research. The EEG data from both alcoholic and non-alcoholic subjects is filtered to remove noise. Then, Principal Component analysis (PCA) is used to reduce the dimensionality of the EEG data while preserving its key features. A hybrid deep learning model named AlcoNet, consisting of CNN (Convolution Neural Network) and BiLSTM (Bidirectional Long Short-Term Memory) with attention mechanism, is designed and trained with PCA-transformed EEG data. The proposed model exhibited a mean accuracy level of 98.34%, showcasing that the proposed work achieved high accuracy in differentiating between the EEG signals of alcoholic and non-alcoholic subjects. The novelty of the research lies in: i) designing a novel deep learning model named ‘AlcoNet’ for automated detection of alcoholism using EEG, ii) incorporating attention mechanism to increase the classification performance of the designed ‘AlcoNet’ model.

关键词： Deep learning Attention mechanisms Accuracy Alcoholism Bidirectional long short term memory Brain modeling Electroencephalography Real-time systems Monitoring Principal component analysis

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Research on Probability of Discharge in Emergency Departments Based on Stochastic Timed Petri Nets and Machine Learning

Research on Probability of Discharge in Emergency Department...

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IEEE International conference on Networking, Sensing and control

作者： Yinglong Wang Wangyang Yu Xianwen Fang Lei Meng Yumeng Cheng Jing Zhang School of Computer Science Shaanxi Normal University Xi'an China School of Mathematics and Big Data Anhui University of Science and Technology Huainan China Information Network Center Qinghai Normal University Xining China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Currently, factors such as hidden health hazards, unhealthy lifestyles, and environmental pollution have led to the frequent occurrence of diseases, causing a common problem - a sharp increase in hospital admissions. However, for emergency departments (EDs), their medical resources and scale are insufficient to meet the challenges of this conventional hospital level. Thus, modeling the diagnostic process of the ED, and applying an appropriate method to predict the probability of discharges within a certain time, is of significant importance for achieving rational resource allocation. Effective allocation of medical resources can ensure that patients receive timely and effective medical care, improve the quality and level of medical services, and thereby enhance patient satisfaction with medical care. Therefore, the modeling tool for this paper is stochastic timed Petri nets (STPN), in order to more accurately simulate various random events and delays that occur within the ED. Additionally, for better analysis of the model, we will convert the time interval values assigned to transitions into time interval values assigned to places, creating a Petri net known as TSdPN. Using Net learning method to predict patient discharge probabilities helps healthcare providers allocate beds, resources, and follow-up medical services more accurately, thereby improving the efficiency of medical resource utilization. Combining these two approaches makes ED operations more intelligent and efficient, providing patients with a better quality medical experience.

关键词： Learning systems Analytical models Hospitals Petri nets Stochastic processes Predictive models User experience Sensors Resource management Medical diagnostic imaging

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