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检索条件"主题词=Filtering algorithms"

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Research on News Recommendation Algorithm Based on Deep Learning

Research on News Recommendation Algorithm Based on Deep Lear...

引用

Artificial Intelligence and Automation Control (AIAC), International Conference on

作者： Danping Qiu Guangdong Baiyun College Guangzhou China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

With the rapid development of Internet technology, the problem of news information overload is becoming increasingly serious. How to efficiently filter out the news that users are interested in has become a current research hotspot. This article mainly discusses the news recommendation algorithm based on deep learning, and constructs an efficient recommendation model by analyzing user behavior data and news content. Research has found that deep learning techniques can significantly improve the accuracy of news recommendations and user satisfaction. This article provides a detailed introduction to the development history of news recommendation systems, the limitations of traditional recommendation algorithms, and the advantages of applying deep learning in news recommendation. In addition, this article also compares and analyzes various deep learning based news recommendation algorithms, and looks forward to future research directions.

关键词： Deep learning Analytical models Automation filtering algorithms Information filters Data models Internet History Artificial intelligence Recommender systems

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Video Smoke Detection Algorithm Based on a Spatial-Temporal Neural Network Model

Video Smoke Detection Algorithm Based on a Spatial-Temporal ...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zhen Cao Xi Zhang Ministry of Emergency Management Shenyang Fire Science and Technology Research Institute Shengyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

a kind of spatial-temporal neural network video smoke detection algorithm is proposed in order to solve the problems associated with the incorrect classification of the static approximate smoke background in the face of the detection of smoke in video detection networks, and the problem of false alarms and of the original test model algorithms being different in different detection environments. Based on the original YOLO v4 neural network algorithm, this paper introduces a k-means + + algorithm and genetic algorithm, while using the algorithm's clustering function to classify the sample points of the real boxes of the image data set, which make it a more suitable anchor. At the same time, the genetic algorithm is used to adjust its anchor in order to allow the generated anchor to adapt to the needs related to smoke detection. In the original neural network model, the dual-stream network model algorithm is used to extract information from the first step of the YOLO algorithm in order to further filter the smoke's characteristics as well as filter out error information, all to improve the detection capabilities of the overall neural network for video smoke fog images. Compared with traditional YOLOv4 networks, the algorithm obtained by the model algorithm has been improved by 8.51°/0. In actual tests, the alarm time requirements of the smoke alarm test program for early fire monitoring and the alarm systems for visual images were improved, and the detection accuracy of the network was also improved based on the assurance of the detection speed, while the performance of the model algorithm was also improved for different scenes.

关键词： YOLO Image recognition Neural networks Clustering algorithms Lighting filtering algorithms Approximation algorithms Information filters Classification algorithms Genetic algorithms

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A Predictive Gradient Based LMS Filter for Micro-Opto-Electro-Mechanical Systems

A Predictive Gradient Based LMS Filter for Micro-Opto-Electr...

引用

International Conference on Electronics Technology (ICET)

作者： Fumin Wang Guo Chai Yonghong Tan Ruili Dong Qingyuan Tan College of Information Electrical and Mechanical Engineering Shanghai Normal University Shanghai China College of Mathematics and Physics Shanghai Normal University Shanghai China School of Informstion Science and Technology Donghua University Shanghai China Dept. of Electrical and Computer Engineering University of Windsor Windsor ON Canada

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

In order to estimate the state of MEMS micromirror in the environment with random noise, in this paper, a so-called predictive gradient based least mean square (PG BLMS) filtering method is proposed. In this filtering method, a stochastic state space model with hysteresis is developed to describe characteristic of MEMS micromirror disturbed by random noise. Then, the PGBLMS filter is developed using the established model to eliminate the influence of random noise. Subsequently, the proposed filtering method is applied to the state estimation of an electromagnetic scanning micromirror disturbed by random noise.

关键词： Micromechanical devices filtering Noise Stochastic processes filtering algorithms Prediction algorithms State estimation

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Fast 3D Object Motion Detection Algorithm Design

Fast 3D Object Motion Detection Algorithm Design

引用

Software Engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Shih-An Li Yun-Hung Ho Ching-Chang Wong Hsuan-Ming Feng Department of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan Department of Computer Science and Information Engineering National Quemoy University Kinmen Taiwan

ISBN: (数字)9798350374346

ISBN: (纸本)9798350374353

In this paper, this paper designs and implements a three-dimensional (3D) LiDAR object motion detection algorithm for real-time identification of 3D object positions in the surrounding environment. We first complete the point cloud preprocessing process to filter the data and then complete the object behaviors detection. In this paper, a passthrough filter and a voxel grid filter are used for the initial screening of point clouds. The point cloud data are then classified into object point clouds and ground point clouds using the Randomized Accordance Random Sampling Consistency (RANSAC) algorithm, and it filters out the ground point cloud. 3D object behavior recognition is completed by object detection algorithms. The DBSCAN algorithm is used as the point cloud clustering algorithm in the object detection system. Establish the center of gravity of the cluster after the clustering is complete. In order to confirm that the objects detected at different time points are the same object, this paper uses the Gale-Shapley algorithm to match the center of gravity of point cloud clustering result. Finally, it is verified that the method proposed in this study can obtain the ability to identify objects quickly, efficiently and correctly.

关键词： Point cloud compression Three-dimensional displays Heuristic algorithms Clustering algorithms Object detection filtering algorithms Prediction algorithms

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A Study on Streamlining and Denoising Point Clouds for Aero-engine Blade Surface Reconstruction

A Study on Streamlining and Denoising Point Clouds for Aero-...

引用

IEEE International Conference on Civil Aviation Safety and Information Technology (ICCASIT)

作者： Ziming Zhang Xinqiang Zhou Tiangong University Tianjin China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

Aiming at the issue of numerous redundant, noisy, and outlier points in the reconstructed point cloud of aero-engine blade surfaces, which significantly affects reconstruction accuracy and efficiency, a point cloud streamlining and denoising algorithm based on multi discriminative parameters and improved Guided Filter is proposed. The algorithm is designed to streamline the numerous redundant points generated by the high-precision acquisition of the blade point cloud. In the feature area, the curvature of each point, the sum of the normal angles between the point and its neighboring points, and the median of these values are calculated. These discriminative parameters are then combined to extract the feature points in the feature area. For the non-feature area, the k -means clustering algorithm is used to extract key points. This algorithm uniformly streamlines the point cloud while preserving the detailed features of the blade surface point cloud data. To effectively eliminate challenging noise and outliers, the geometric features of the point cloud are used as weights for the overall smoothing and denoising of the model. The results show that after processing the point cloud data with the streamlining and denoising algorithm and then triangulating it, the surface reconstruction quality significantly improves. Specifically, the accuracy increases by $38.46 \%$, and the processing efficiency improves by $\mathbf{3 6 . 0 7 \%}$. This enhancement has led to more precise path planning for the ultrasonic nondestructive flaw detection robot used on aero-engine blades, thereby improving flaw detection accuracy and efficiency.

关键词： Point cloud compression Surface reconstruction Accuracy Blades Noise reduction Clustering algorithms filtering algorithms Feature extraction Aircraft propulsion Surface treatment

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Research on Dynamic Obstacle Recognition Algorithm based on Millimeter-Wave Radar

Research on Dynamic Obstacle Recognition Algorithm based on ...

引用

International Conference on Systems and Informatics (ICSAI)

作者： Lizhou Zhu Huabin Yan Chen Gao Jiming Sa School of Information Engineering Wuhan University of Technology Wuhan Hubei China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Due to the low-lying unevenness of the road surface, multipath propagation, reflection of small objects, etc, the final radar image is presented when there are a large number of non-moving objects or small objects to interfere in the use of millimeter-wave radar for the detection of dynamic obstacles. Aiming at the above situation, an optimized filtering algorithm for moving large objects is proposed, using Kalman filtering and radar reflection cross-sectional area for comprehensive analysis, to exclude the interface ghosting and interference caused by stationary or small objects. The experimental results show that the method has high recognition and accuracy.

关键词： Radar cross-sections Accuracy Runtime Heuristic algorithms Radar detection Interference Millimeter wave radar filtering algorithms Radar imaging Kalman filters

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A Five-Element USBL Location Method Based on DNN-Kalman Filter

A Five-Element USBL Location Method Based on DNN-Kalman Filt...

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Yudong Su Kun Ye Haixin Sun School of Informatics Xiamen University Xiamen China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Time Difference of Arrival (TDOA) is used for traditional Ultra-Short Baseline (USBL) positioning. Due to the short baseline length between acoustic arrays, the positioning accuracy is reduced in the case of low SNR. In order to solve this problem, this paper focuses on the phase information of underwater acoustic signal instead of using TDOA localization method. In this paper, five-element USBL array is used to filter underwater acoustic signals by means of long and short baseline multiplexing, deep neural network (DNN) and Kalman filtering algorithm. The simulation results show that the proposed method can effectively improve the positioning accuracy, and the positioning performance is much better than the traditional algorithm under the condition of low SNR.

关键词： Accuracy Time difference of arrival Simulation Artificial neural networks filtering algorithms Information filters Acoustic arrays Kalman filters Underwater acoustics Signal to noise ratio

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Wheel Speed Identification and Longitudinal Vehicle Speed Estimation for Four-Wheel Drive Vehicles

Wheel Speed Identification and Longitudinal Vehicle Speed Es...

引用

Conference on Vehicle Control and Intelligence (CVCI)

作者： Dongmei Wu Zixuan Liu Taowei Yan Pei Zhang Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan University of Technology Wuhan China Dongfeng Research and Development Center Passenger Vehicle Platform Center Dongfeng Motor Corporation Wuhan China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

To address the issue of vehicle speed estimation, particularly at low speeds, applicable to various environmental conditions, especially in the absence of driven wheels for vehicles equipped with four-wheel drive systems, a rule-based wheel speed identification and vehicle speed estimation method is proposed. This technique leverages wheel speed sensors and longitudinal acceleration sensors for vehicles to enhance the precision of wheel speed identification at low speeds and enhance the adaptability of vehicle speed estimation under different conditions. Firstly, to tackle the problem of no wheel speed pulse signal passing within one sampling period at low speeds, estimation is achieved through time accumulation, enabling effective wheel speed identification for both high and low-speed. Furthermore, utilizing the Kalman filtering algorithm and Butterworth filter, the wheel speed and wheel acceleration are filtered separately to reduce signal fluctuations caused by noise interference, thereby providing reliable input signals for estimating vehicle speed. Building upon this, A method for estimating vehicle speed based on the gradient constraint approach is proposed, taking into account the sequential relationships of each wheel speed and the longitudinal acceleration signal. Finally, real vehicle tests under different conditions demonstrate that the proposed vehicle speed estimation technique achieves high estimation accuracy, meeting the vehicle's speed estimation requirements under various environmental factors.

关键词： Fluctuations Noise Buildings Wheels Estimation Interference filtering algorithms Environmental factors Sensors Kalman filters

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Improved Adaptive Fading Memory Filter Based on State Switching Under Anomalous Measurement Data

Improved Adaptive Fading Memory Filter Based on State Switch...

引用

Digital Society and Intelligent Systems (DSInS), International Conference on

作者： Aihua Shan Yajie Hou Hui He Bo Sun College of Electronic Information Engineering Shandong University of Science and Technology Qingdao China Hunan Huahuit Automation Technology Co. Changsha China College of Intelligent Equipment Shandong University of Science and Technology Taian China

ISBN: (数字)9798331528829

ISBN: (纸本)9798331528836

With the swift evolution of integrated navigation technology in our country, it is essential to realize more accurate positioning of integrated navigation systems. In this paper, the INS/GNSS integrated navigation method is studied. Firstly, the system model is established according to the INS/GNSS integrated navigation principle. Then, based on the system model, the data fusion algorithms of Kalman filter memory filter (KF), FKF, and adaptive filtering are studied, to solve the problem of excessive use of fading factor in fading memory filtering, there is a state-switching-based fading memory filtering method (MFKF), however, this algorithm only considers the imprecise modeling of the Integrated Navigation System or the mismatch of the noise characteristics and does not consider the situation of the abnormal measurement data. To solve this problem, an improved adaptive fading memory filter based on state switching (IMFKF) is proposed by combining the innovation-based adaptive filtering method with the state switching fading memory filter and building the algorithm model that is more suitable for precise positioning. Simulation experiments verify the validity of the algorithm.

关键词： Fading channels Technological innovation Adaptive systems filtering Current measurement Noise Sea measurements Switches filtering algorithms Noise measurement

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Detection of the Moment of Mode Change in Differential Protection Based on Wavelet Transform

Detection of the Moment of Mode Change in Differential Prote...

引用

International Youth Scientific and Technical Conference on Relay Protection and Automation (RPA)

作者： V.A. Solovyev T.G. Klimova E.N. Kolobrodov Moscow Power Engineering Institute Russian Federation

ISBN: (数字)9798331501105

ISBN: (纸本)9798331501112

In the context of designing relay protection and primary equipment, current transformers (CT) for relay protection purposes were previously selected based on checking their error in the steady-state short-circuit mode. Due to a number of accidents associated with incorrect operation of MP relay protection in the transient short-circuit mode, a requirement for correct operation in the transient mode was introduced for microprocessor relay protection [1].

关键词： Wavelet transforms Current transformers Noise Protective relaying filtering algorithms Stability analysis Steady-state Protection Transient analysis Computational complexity

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