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检索条件"主题词=Filtering algorithms"

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Research on human posture recognition based on multi-feature fusion chunk sampling particle filtering algorithm

Research on human posture recognition based on multi-feature...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Yufei Zhao Naval Architecture and Ocean Engineering College Dalian Maritime University Dalian China

As a developing research direction in the field of computer vision, human posture recognition(HPR) involves many related technologies such as pattern recognition, artificial intelligence, image processing and machine vision. This paper focuses on the application of multi feature fusion(MFF) and block sampling(BS) particle filter algorithm(PFA) in HPR, and briefly analyzes the design of human posture data acquisition terminal, the analysis and extraction of human contour features, and the analysis and extraction of stride features; This paper discusses the MFF and BS PFA, and applies it to HPR. Through the statistical experiments of recognition rate(RR) and sensitivity of virtual reality actions, the effectiveness of MFF and BS PFA applied to HPR is verified.

关键词： Sensitivity Heuristic algorithms Machine vision Virtual reality filtering algorithms Feature extraction Information filters

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Early Cancer Detection and Diagnosis using Deep Learning Models in Medical Imaging

Early Cancer Detection and Diagnosis using Deep Learning Mod...

引用

Advancement in Electronics & Communication Engineering (AECE), International Conference on

作者： Shailendra Singh Sikarwar Rachna Rathore Sharma Sonu Kumar Mitali Monalisha Lopamudra Satapathy Nisha M Dept. of Computer Science PGV College Jiwaji University Gwalior India Dept. of Mathematics Jiwaji University Gwalior India Dept. of Computer Engineering and Applications Mangalayatan University Aligarh India Dept. of Paramedical Science Usha Martin University Ranchi India Dept. of Agriculture Usha Martin University Ranchi India Dept. of CSE Dhanalakshmi Srinivasan University Coimbatore India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Breast cancer is a leading cause of mortality among women globally. While its exact cause remains uncertain, no definitive methods exist for its prevention or treatment. However, early detection through mammography plays a crucial role in improving management strategies and increasing the likelihood of recovery. Mammography aids not only in early cancer detection but also in identifying various conditions and distinguishing between cancer types—benign, malignant, or normal. This study explores an advanced machine learning and image processing framework for detecting and classifying breast cancer. The approach incorporates machine learning techniques alongside feature extraction, selection, and image preprocessing to classify breast cancer and related skin diseases effectively. Key advancements include the use of a geometric mean filter to enhance image quality and AlexNet for feature extraction. The relief algorithm is employed for feature selection, and classification is performed using machine learning models such as random forest, K-nearest neighbors (KNN), least square support vector machine (LS-SVM), and Naïve Bayes. This innovative methodology provides an accurate and efficient solution for breast cancer detection through advanced image analysis techniques.

关键词： Support vector machines Image quality Machine learning algorithms Mortality Nearest neighbor methods filtering algorithms Feature extraction Breast cancer Classification algorithms Random forests

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A Fault Diagnosis Method for NPC Three-Level Inverters Based on VMD-SE and SMA-SVM

A Fault Diagnosis Method for NPC Three-Level Inverters Based...

引用

Chinese Automation Congress (CAC)

作者： Baoye Song Mengru Zhao Qizheng Zhang College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

To tackle the challenges of low accuracy and high computational complexity in diagnosing open-circuit faults for NPC three-level inverters, this paper introduces a novel fault diagnosis approach that integrates Variational Mode Decomposition (VMD), Sample Entropy (SE), and an enhanced Slime Mold Algorithm (SMA) optimized Support Vector Machine (SVM). The method begins by decomposing the fault current signal using VMD and calculating the SE to identify fault characteristics. Next, it employs a correlation coefficient to filter out invalid Intrinsic Mode Functions (IMFs). Finally, an improved SMA is applied to optimize the SVM for accurate fault classification and identification. Simulation results show that this method outperforms traditional techniques in terms of effectiveness and accuracy.

关键词： Fault diagnosis Support vector machines Accuracy Simulation Neural networks filtering algorithms Inverters Entropy Vectors Fault currents

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Visual Effects of Stage Performance using Pelican Optimization Algorithm with Long Short-Term Memory

Visual Effects of Stage Performance using Pelican Optimizati...

引用

Data Science and Information System (ICDSIS), IEEE International Conference on

作者： Jin Song College of Music Education Xi’an Conservatory of Music Xi’an China

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Nowadays, new media technology has widely entered art forms such as film and television that has changed visual effect way in new media environment. This research proposed a Pelican Optimization Algorithm with Long ShortTerm Memory (POA-LSTM) for the visual effect of stage performance. The MNIST dataset is used here and it is preprocessed by median filter. Then, the POA is used as optimization algorithm and LSTM is used as classifier. The LSTM is effective for predicting sequential data and it is mainly designed for time-series data. By providing previous and current sequences data of visual effects, LSTMs predict the appropriate upcoming sequence. It enables the effective and consistent output of visuals with limited to no differences in appearance. Moreover, the POA optimize the LSTM to generate an accurate prediction. The POA optimize the hyper parameters, thereby ensuring that the LSTM achieves accurate prediction results for visual effects. The f1-score, precision, accuracy and recall are used for evaluating POA-LSTM performance. The POA-LSTM achieves $95.34 \%$ f1-score, $96.53 \%$ precision, $97.61 \%$ accuracy, $97.28 \%$ recall when compared to state-of-art methods.

关键词： Accuracy TV Metaheuristics Media filtering algorithms Visual effects Prediction algorithms

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Continuity Path Planning for UAV with Enhanced Douglas-Peucker Algorithm

Continuity Path Planning for UAV with Enhanced Douglas-Peuck...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Simo Lv Renkai Yi Xiuhui Peng Liyan Wen College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper proposes a continuous path planning method, where Deep Q-network (DQN) is employed for initial grid map path planning, followed by the enhanced Douglas-Peucker (DP) algorithm to address obstacles, and culminating in the generation of discrete path points using curvature-continuous Bézier curves. First, for grid maps, an unmanned aerial vehicle (UAV) path planning method is designed, utilizing the DQN algorithm to generate a discrete set of path points. Second, an enhanced DP algorithm is used to filter the discrete path points in an environment with obstacles. Then, the filtered set of path points is smoothed using Bézier curves with continuous curvature to conform to the flight physical characteristics of the fixed-wing UAV. Finally, the effectiveness of the proposed path planning and smoothing algorithm is verified through numerical simulations and comparative simulations in a grid map environment with random obstacles.

关键词： Training Smoothing methods filtering Autonomous systems filtering algorithms Autonomous aerial vehicles Numerical simulation Trajectory Numerical models

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Image Enhancement Method for Smart Grid Visual Monitoring System

Image Enhancement Method for Smart Grid Visual Monitoring Sy...

引用

Chinese Control and Decision Conference, CCDC

作者： Aixia Wang Ziyang Chen Jingrong Chen School of Information Science and Engineering Northeast University of China ShenYang People's Republic of China Beijing Microelectronics Tech Institution (BMTI) Northeast University of China BeiJing People's Republic of China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

In the field of video surveillance in the smart grid, monitoring room staff are faced with the problem of being unable to see low or excessive illumination areas clearly due to diverse lighting conditions in the monitoring scene. To allow them to observe more image details, it is necessary to increase the contrast of these areas and improve the visibility of the images. This paper proposes a method to enhance uncertain illumination images. Our solution is to decompose the image by filtering to enhance the details, and adjust the brightness with an adaptive scheme. First, the Guide Filter (GF) is used to decompose the input image into a base layer and a detail layer. After filtering, the detail layer is stretched by the arctan function and adjusted by the light intensity of the base layer, and then the adjustment result is linearly combined with the base layer. Finally, the brightness is adjusted by using the adaptive curve guided by "maximum entropy" to improve the contrast in low illumination areas and excessive illumination areas. Experimental results show that this algorithm can significantly improve the quality of images with uneven illumination, more details in low illumination areas and excessive illumination areas can be observed, and it has the advantage of lower running time consumption over some popular algorithms.

关键词： Visualization filtering Brightness Lighting filtering algorithms Video surveillance Entropy

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Hardware-Accelerated Real-Time Detection of Catenary Geometry Parameters

Hardware-Accelerated Real-Time Detection of Catenary Geometr...

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Ximeng Han Quanli Liu Wei Wang Xiaoguang Zhao Zhigang Wang School of Control Science and Engineering Dalian University of Technology Dalian China Dalian Seasky Automation Co. Ltd Dalian China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Dynamic detection of catenary geometry parameters (CGP) has a significant impact on the stability of the train power supply. Aiming at the problem that the detection speed of traditional detection methods can not meet the high frequency, high speed, and low fitness, this paper presents a non-contact, high-precision, real-time detection system for the CGP based on FPGA hardware acceleration. Dynamic measurement techniques based on monocular cameras and line-structured light. Firstly, the FPGA controls the camera to acquire images in real-time, and a parallel three-step median filtering algorithm is developed to reduce image noise, ten times more efficiently than the traditional median filter. Secondly, in order to solve the catenary localization problem in complex scenes, Hough transform is utilized to detect catenary, and the mean-shift algorithm is improved according to the hardware structure to realize catenary localization and tracking in complex scenes. Additionally, the weighted centroid extraction algorithm combined with hardware acceleration is optimized to achieve the stable extraction of pixel coordinates. Finally, the FPGA migration of the algorithms is completed. The coordinates of the spot center are transmitted to the host via Ethernet, and CGP is calculated by reconstructing the line structured light in 3D. After testing an image with a resolution of 800*480, Modelsim simulation takes 0.02 ms to process one image frame. When the algorithm is transplanted to the FPGA platform, the process from sending the control word to receiving the center of mass coordinates takes only 25 ms. The experiments demonstrate that the detection system outlined in this paper exhibits high accuracy and speed, adaptability, portability, and ease of installation, making it highly valuable for practical applications.

关键词： Geometry Location awareness filtering algorithms Feature extraction Cameras Real-time systems Field programmable gate arrays

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Interactive Multiple Model Filter Algorithm for Multi-Object Tracking of Unmanned Surface Vessels

Interactive Multiple Model Filter Algorithm for Multi-Object...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Hao Weng Yang Zhou Wentai Xu Zhenglin Li Institute of Artifcial Intelligence Shanghai University Shanghai China Research Institute of USV Engineering School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

Multi-object tracking algorithm based on deep learning has made rapid development in the field of pedestrian tracking. However, there are few tracking studies of unmanned surface vessels in the marine field, some research institutions have attempted to integrate YOLOv5 DeepSORT for unmanned surface vessels tracking. The motion of unmanned surface vessels exhibits nonlinear characteristics. However, the Kalman filter algorithm assumes linear motion of objects. The frame rate of the videos in the MOT datasets is 25 FPS, and the pedestrian movement is slow. The frame rate of unmanned surface vessels videos is low, and unmanned surface vessels movement faster compared to pedestrians. The position of the same object changes greatly in the consecutive frames. Consequently, traditional Kalman filter algorithms and IOU for association match results in poor performance in multiobject tracking for unmanned surface vessels. To address this issue, we propose an interactive multiple model (IMM) filter algorithm, and the position metric employ Euclidean distance. This method demonstrates stronger robustness and accuracy on a self-built datasets for unmanned surface vessels tracking. Experimental results demonstrate that the proposed method significantly improves tracking performance: MOTA increases by 6.2%, HOTA by 7.4%, IDF1 by 7.5%, and IDSW decreases by 100 instances. The method shows strong performance in tracking unmanned surface vessels.

关键词： Measurement Pedestrians Accuracy Tracking Euclidean distance filtering algorithms Prediction algorithms Robustness Kalman filters Videos

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Indoor Cooperative Localization Algorithm Based on UWB/PDR Data Fusion

Indoor Cooperative Localization Algorithm Based on UWB/PDR D...

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Semiconductor and Electronic Technology (ISSET), International Symposium on

作者： Longquan Sun Jiaxing He Lizhen Cui Digital Intelligence Industry College Inner Mongolia University of Science and Technology Baotou Inner Mongolia China

ISBN: (数字)9798350390285

ISBN: (纸本)9798350390292

In recent years, there is an increasing demand for indoor localization, and multi-source data fusion algorithms have gradually become a research hotspot. Ultra-wideband (UWB) technology is widely used for its high accuracy, but the effect decreases significantly in non-line-of-sight (NLOS) environments. Pedestrian Dead Reckoning (PDR) technology estimates step length and direction from sensor data, which has high localization accuracy in a short period of time, but the problem of error accumulation is serious with the increase of time. To address these problems, this paper proposes a nested Extended Kalman Filter (N-EKF) that fuses the ranging information of UWB with the step length and heading angle values of PDR to obtain the pedestrian position coordinates. The experimental results show that the fusion algorithm reduces the average error by 75% and 52.2% compared to the UWB and PDR localization algorithms, which effectively alleviates the excessive UWB ranging error in weak NLOS environments.

关键词： Location awareness Accuracy Pedestrians Fuses Data integration Interference filtering algorithms Distance measurement Kalman filters Ultra wideband technology

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Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs algorithms

Developing Cryptocurrency Trading Strategy Based on Autoenco...

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Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Zhuohuan Hu Richard Yu Zizhou Zhang Haoran Zheng Qianying Liu Yining Zhou Independent Researcher China Marvellous Creature Inc Canada UIUC IL USA University of Pennsylvania Philadelphia PA USA Independent Researcher MA USA Texas A&M University TX USA

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

This paper leverages machine learning algorithms to forecast and analyze financial time series. The process begins with a denoising autoencoder to filter out random noise fluctuations from the main contract price data. Then, one-dimensional convolution reduces the dimensionality of the filtered data and extracts key information. The filtered and dimensionality-reduced price data is fed into a GANs network, and its output serve as input of a fully connected network. Through cross-validation, a model is trained to capture features that precede large price fluctuations. The model predicts the likelihood and direction of significant price changes in real-time price sequences, placing trades at moments of high prediction accuracy. Empirical results demonstrate that using autoencoders and convolution to filter and denoise financial data, combined with GANs, achieves a certain level of predictive performance, validating the capabilities of machine learning algorithms to discover underlying patterns in financial sequences.

关键词： Analytical models Machine learning algorithms Accuracy Autoencoders Time series analysis Predictive models filtering algorithms Information filters Real-time systems Cryptocurrency

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