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  • Open Access

    ARTICLE

    Maximum Correntropy Criterion-Based UKF for Loosely Coupling INS and UWB in Indoor Localization

    Yan Wang*, You Lu, Yuqing Zhou, Zhijian Zhao

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2673-2703, 2024, DOI:10.32604/cmes.2023.046743

    Abstract Indoor positioning is a key technology in today’s intelligent environments, and it plays a crucial role in many application areas. This paper proposed an unscented Kalman filter (UKF) based on the maximum correntropy criterion (MCC) instead of the minimum mean square error criterion (MMSE). This innovative approach is applied to the loose coupling of the Inertial Navigation System (INS) and Ultra-Wideband (UWB). By introducing the maximum correntropy criterion, the MCCUKF algorithm dynamically adjusts the covariance matrices of the system noise and the measurement noise, thus enhancing its adaptability to diverse environmental localization requirements. Particularly in… More >

  • Open Access

    ARTICLE

    A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

    Jie Li1,3,*, Rongwen Wang2, Yongtao Hu1,3, Jinjun Li1

    Structural Durability & Health Monitoring, Vol.18, No.1, pp. 73-90, 2024, DOI:10.32604/sdhm.2023.044023

    Abstract The aging prediction of railway catenary is of profound significance for ensuring the regular operation of electrified trains. However, in real-world scenarios, accurate predictions are challenging due to various interferences. This paper addresses this challenge by proposing a novel method for predicting the aging of railway catenary based on an improved Kalman filter (KF). The proposed method focuses on modifying the priori state estimate covariance and measurement error covariance of the KF to enhance accuracy in complex environments. By comparing the optimal displacement value with the theoretically calculated value based on the thermal expansion effect… More > Graphic Abstract

    A Novel Method for Aging Prediction of Railway Catenary Based on Improved Kalman Filter

  • Open Access

    ARTICLE

    Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

    Othman S. Al-Heety1,*, Zahriladha Zakaria1,*, Ahmed Abu-Khadrah2, Mahamod Ismail3, Sarmad Nozad Mahmood4, Mohammed Mudhafar Shakir5, Sameer Alani6, Hussein Alsariera1

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2103-2127, 2024, DOI:10.32604/cmes.2023.029509

    Abstract Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision. In this article, these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data. The framework integrates Kalman filtering and Q-learning. Unlike smoothing Kalman filtering, our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error. Unlike traditional Q-learning, our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from… More >

  • Open Access

    ARTICLE

    Notes on Convergence and Modeling for the Extended Kalman Filter

    Dah-Jing Jwo*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2137-2155, 2023, DOI:10.32604/cmc.2023.034308

    Abstract The goal of this work is to provide an understanding of estimation technology for both linear and nonlinear dynamical systems. A critical analysis of both the Kalman filter (KF) and the extended Kalman filter (EKF) will be provided, along with examples to illustrate some important issues related to filtering convergence due to system modeling. A conceptual explanation of the topic with illustrative examples provided in the paper can help the readers capture the essential principles and avoid making mistakes while implementing the algorithms. Adding fictitious process noise to the system model assumed by the filter… More >

  • Open Access

    ARTICLE

    Kalman Filter-Based CNN-BiLSTM-ATT Model for Traffic Flow Prediction

    Hong Zhang1,2,*, Gang Yang1, Hailiang Yu1, Zan Zheng1

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1047-1063, 2023, DOI:10.32604/cmc.2023.039274

    Abstract To accurately predict traffic flow on the highways, this paper proposes a Convolutional Neural Network-Bi-directional Long Short-Term Memory-Attention Mechanism (CNN-BiLSTM-Attention) traffic flow prediction model based on Kalman-filtered data processing. Firstly, the original fluctuating data is processed by Kalman filtering, which can reduce the instability of short-term traffic flow prediction due to unexpected accidents. Then the local spatial features of the traffic data during different periods are extracted, dimensionality is reduced through a one-dimensional CNN, and the BiLSTM network is used to analyze the time series information. Finally, the Attention Mechanism assigns feature weights and performs… More >

  • Open Access

    ARTICLE

    Vehicle Detection and Tracking in UAV Imagery via YOLOv3 and Kalman Filter

    Shuja Ali1, Ahmad Jalal1, Mohammed Hamad Alatiyyah2, Khaled Alnowaiser3, Jeongmin Park4,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114

    Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal… More >

  • Open Access

    ARTICLE

    Sensor-Based Adaptive Estimation in a Hybrid Environment Employing State Estimator Filters

    Ashvini Kulkarni1,2, P. Augusta Sophy Beulet1,*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 127-146, 2023, DOI:10.32604/iasc.2023.035144

    Abstract It is widely acknowledged that navigation is a significant source of between sites. The Global Positioning System (GPS) has numerous navigational advancements, and hence it is used widely. GPS navigation can be compromised at any level between position, location, and estimation, to the detriment of the user. Consequently, a navigation system requires the precise location and underpinning tracking of an object without signal loss. The objective of a hybrid environment prediction system is to foresee the location of the user and their territory by employing a variety of sensors for position estimation and monitoring navigation.… More >

  • Open Access

    ARTICLE

    A Non-singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

    Aoqi Xu1, Khalid A. Alattas2, Nasreen Kausar3, Ardashir Mohammadzadeh4, Ebru Ozbilge5,*, Tonguc Cagin5

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 17-32, 2023, DOI:10.32604/iasc.2023.036623

    Abstract In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs. noisy environments. The primary objective of this study is to present a more robust method against uncertainties. This paper proposes a new deep learning scheme for modeling and identification applications. The suggested approach is based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support measurement errors and high-level uncertainties. Besides the rule optimization, the antecedent parameters and the level of secondary memberships are also adjusted by the suggested square More >

  • Open Access

    ARTICLE

    Scale Invariant Feature Transform with Crow Optimization for Breast Cancer Detection

    A. Selvi*, S. Thilagamani

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2973-2987, 2023, DOI:10.32604/iasc.2022.029850

    Abstract Mammography is considered a significant image for accurate breast cancer detection. Content-based image retrieval (CBIR) contributes to classifying the query mammography image and retrieves similar mammographic images from the database. This CBIR system helps a physician to give better treatment. Local features must be described with the input images to retrieve similar images. Existing methods are inefficient and inaccurate by failing in local features analysis. Hence, efficient digital mammography image retrieval needs to be implemented. This paper proposed reliable recovery of the mammographic image from the database, which requires the removal of noise using Kalman More >

  • Open Access

    ARTICLE

    Heartbeat and Respiration Rate Prediction Using Combined Photoplethysmography and Ballisto Cardiography

    Valarmathi Ramasamy1,*, Dhandapani Samiappan2, R. Ramesh3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1365-1380, 2023, DOI:10.32604/iasc.2023.032155

    Abstract Owing to the recent trends in remote health monitoring, real-time applications for measuring Heartbeat Rate and Respiration Rate (HARR) from video signals are growing rapidly. Photo Plethysmo Graphy (PPG) is a method that is operated by estimating the infinitesimal change in color of the human face, rigid motion of facial skin and head parts, etc. Ballisto Cardiography (BCG) is a nonsurgical tool for obtaining a graphical depiction of the human body’s heartbeat by inducing repetitive movements found in the heart pulses. The resilience against motion artifacts induced by luminance fluctuation and the patient’s mobility variation… More >

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