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检索条件"主题词=Filtering algorithms"

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Restaurant Recommendation System using Machine Learning algorithms

Restaurant Recommendation System using Machine Learning Algo...

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Shefali Goyal Nongmeikapam Thoiba Singh Iesh Bajetha Sukhpreet Singh Himanshu Singh Prashant Kumar Department of Computer Science And Engineering Chandigarh University Punjab India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The necessity for recommendation systems spans across various domains, not least in the realm of dining and exploring new locales. Restaurants, in particular, benefit from such systems to both attract customers and offer them a chance to indulge in preferred and renowned dishes. Discovering these dishes, especially in unfamiliar areas, poses a significant challenge. This paper introduces a restaurant recommendation system that leverages food and service rating distributions to calculate matrix density. Additionally, we implement a popularity-based model to suggest restaurants to patrons. A ranking mechanism is utilized, based on scoring, to recommend top-rated restaurants and their most popular dishes. We use singular value decomposition and collaborative filtering to improve the model. The model’s performance is assessed using the RMSE metric. This study is conducted using data from Kaggle and includes the development of a web-based application created with Python’s Flask framework.

关键词： Measurement Knowledge engineering Machine learning algorithms Communication systems Collaborative filtering filtering algorithms Information filters

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Adaptive Regression Trees for Nonlinear Adaptive filtering

Adaptive Regression Trees for Nonlinear Adaptive Filtering

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Asilomar Conference on Signals, Systems & Computers

作者： Jarren T. Worthen Todd K. Moon Jacob H. Gunther Space Dynamics Labratory Electrical & Computer Engineering Dept Utah State University

Decision trees provide interperable parametric nonlinear models with effective algorithms to train the models on a fixed set of training data. In this paper we devise a method of making these models adaptive, so they adjust to data with changing statistics. This is accomplished by partitioning the trees in the model into sets called groves, then aging out groves, similar to the aging of errors in a recursive least-squares models. As old groves age out, new groves are introduced which compensate both for the changes in statistics of the data and the aging of the data. The adaptive algorithm is tested on a prediction problem on a simulated nonlinear signal.

关键词： Adaptation models filtering Training data Forestry Aging filtering algorithms Prediction algorithms

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A Rao-Blackwellized Particle Filter for Superelliptical Extended Target Tracking

A Rao-Blackwellized Particle Filter for Superelliptical Exte...

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International Conference on Information Fusion

作者： Oğul Can Yurdakul Mehmet Çetinkaya Enescan Çelebi Emre Özkan Department of Electrical & Electronics Engineering Middle East Technical University Ankara Turkey

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

In this work, we propose a new method to track extended targets of different shapes such as ellipses, rectangles and rhombi. We provide an analytical framework to express these shapes as superelliptical contours and propose a Bayesian filtering scheme that can handle measurements from the contour of the object. The method utilizes the Rao-Blackwellized particle filtering algorithm with novel sensor-object geometry constraints. The success of the algorithm is demonstrated using both simulations and real-data experiments, and the algorithm has been demonstrated to be of high performance in various challenging scenarios.

关键词： Geometry Analytical models Target tracking Shape Atmospheric measurements Shape measurement filtering algorithms Particle measurements Mathematical models Particle filters

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Feature-Aided UKF-PHD Filter for Nonlinear Multi-target Tracking in Holographic Staring Radar

Feature-Aided UKF-PHD Filter for Nonlinear Multi-target Trac...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： Junyao Yang Zebin Chen Zhanming Zhong Yue Zhang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Traditional radar multi-target tracking relies on data association methods, which result in exponential computational complexity when clutter and target densities are high. The Gaussian mixture probability hypothesis density (GM-PHD) filter, built upon the random finite set framework, presents a more efficient approach for multi-target tracking without having to conduct explicit data association. However, this method is only applicable to Gaussian linear models, limiting its utility in practical scenarios. Moreover, radar environments often feature high clutter densities and low detection probabilities, making real-time and accurate target tracking challenging. To address these issues, this paper proposes extending the GM-PHD filter to nonlinear Gaussian models using the unscented transform and introduces a feature-aided unscented Kalman filter PHD (FAUKF-PHD) multi-target tracking algorithm. By leveraging the high-precision Doppler information contained in holographic staring radar echoes, the proposed algorithm adjusts the weights of Gaussian components, significantly reducing clutter weights to distinguish targets from clutter and pruning redundant components. The simulation outcomes evidently show that the FAUKF-PHD filter put forward in this study boosts the estimation accuracy and real-time performance under scenarios characterized by high clutter density and low detection probability.

关键词： Maximum likelihood detection Target tracking Radar clutter Nonlinear filters filtering algorithms Radar tracking Information filters Real-time systems Doppler radar Clutter

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Temperature and Gas Concentration Imaging of LAS Tomography from Gaussian Filtered SART with Artifact Removal

Temperature and Gas Concentration Imaging of LAS Tomography ...

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IEEE International Workshop on Imaging Systems and Techniques (IST)

作者： Jinting Wen Zhang Cao Lijun Xu School of Instrumentation and Optoelectronic Engineering Beihang University Beijing China

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

Laser absorption spectroscopy (LAS) is widely applied for tomographic imaging of temperature and gas concentration in combustion fields. To improve the imaging accuracy and reduce artifacts, the Gaussian filter is added in the iteration of simultaneous algebraic reconstruction technique (SART) as a smoothing operator, and an artifact removal method is developed based on the correlation between flame temperature and concentration. Symmetrical and asymmetrical distributions of temperature and $\mathrm{H}_{2}\mathrm{O}$ mole fraction obtained from the computational fluid dynamics (CFD) simulation were reconstructed in the numerical verification. The proposed Gaussian filtered SART with artifact removal performed well with the fewest artifacts and lowest reconstruction errors compared to typical SART-based methods. The exit of a high-temperature wind tunnel was measured for experiment verification, and temperatures at the center of the wind tunnel exit in 11 different cases were measured by the thermocouple as a reference. Temperature and $\mathbf{H}_{2}\mathbf{O}$ mole fraction distributions reconstructed by the proposed method were smooth, and its variation trend was consistent with the real condition. The maximum relative error of temperature of the proposed method was only 5.29%, much smaller than that of the typical SART with a median filter. Both numerical and experiment verifications demonstrated the ability of the proposed method in high-accuracy tomographic imaging.

关键词： Temperature measurement Temperature distribution Spectroscopy Correlation Wind tunnels Computational fluid dynamics Thermocouples Tomography filtering algorithms Image reconstruction

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A Finite- Time Complementary Filter for Attitude Estimation Using Inertial Sensors

A Finite- Time Complementary Filter for Attitude Estimation ...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Linan Wang Guanghui Wen Xiaojian Yi Xinhe Wang Geng Liu Department of Systems Science Southeast University Nanjing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China School of Cyber Science and Engineering Southeast University Nanjing China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing China

ISBN: (数字)9798350384185

ISBN: (纸本)9798350384192

This paper presents a novel finite-time complementary filter specifically designed for attitude and gyroscope bias estimation. The key contributions of this filter include ensuring rapid convergence to the actual attitude within a finite time, significantly improving performance over traditional filters that only guarantee asymptotic stability. The filter achieves high accuracy by effectively integrating data from multiple sensors, such as gyroscopes, accelerometers, and magnetometers, through advanced sensor fusion algorithms and noise suppression techniques. Additionally, simulation tests confirm the filter's robust resistance to noise, highlighting its potential to enhance the performance of attitude estimation systems across various applications by providing faster, more accurate, and reliable estimates.

关键词： Accuracy Magnetometers Magnetic separation Magnetic sensors Noise reduction Noise filtering algorithms Sensor fusion Gyroscopes Convergence

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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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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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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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