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检索条件"主题词=filtering algorithms"

共 10367 条记录，以下是911-920 订阅

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A Target Tracking Method Based on Multi Nonlinear Filter Fusion for UAV Swarm

A Target Tracking Method Based on Multi Nonlinear Filter Fus...

引用

International Conference on Artificial Intelligence and Computer Information Technology (AICIT)

作者： Zhenzhen Fan Information Engineering Department Shandong Vocational College of Light Industry Zibo Shandong China

ISBN: (数字)9798350365443

ISBN: (纸本)9798350365450

Considering target tracking has an important impact on UA V Swarm. On the basis of the typical nonlinear filtering target tracking algorithm, a multi nonlinear filtering fusion target tracking method has been proposed. The algorithm selects the nonlinear filtering algorithm intelligently by setting the timeliness factor, accuracy factor and the available swarm nodes numbers. The simulation results show that the multi nonlinear filter fusion method can improve the timeliness of target tracking in a certain accuracy range, and the selection factor has a correlation with the tracking accuracy.

关键词： Target tracking Accuracy Simulation Software algorithms Nonlinear filters filtering algorithms Autonomous aerial vehicles Software Information technology Optimization

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Data-Driven State Estimation for Linear Systems

Data-Driven State Estimation for Linear Systems

引用

European Control Conference (ECC)

作者： Vikas Kumar Mishra Sandesh Athni Hiremath Naim Bajcinca Department of Mechanical and Process Engineering RPTU Kaiserslautern Kaiserslautern Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

We study the problem of estimating the states of a linear system based on measured data. We investigate the problem in both deterministic and stochastic settings. In the deterministic case, we develop data-driven conditions under which we can reconstruct state trajectories uniquely. Also, we discuss the case in which we have some missing data in the given input/output measurements. In the stochastic case, we develop a Kalman filter-like algorithm to recursively estimate both states and outputs. Finally, we consider a multi-input multi-output system to elucidate the developed results.

关键词： Linear systems Europe filtering algorithms Vectors Trajectory Kalman filters Noise measurement

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Optimal Design of a 2nd-Order Active Low-Pass Filter Rauch Structure using Genetic Algorithm

Optimal Design of a 2nd-Order Active Low-Pass Filter Rauch S...

引用

Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Maaroufi Ismail El Beqal Asmae Benhala Bachir Faculty of sciences Dhar El Mahraz Sidi Mohammed Ben Abdellah University Fez Morocco

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

The values of discrete components (resistors and capacitors) which are selected in industrial series according to chosen constant values contribute strongly to the performance of analog filters. A complete search of all possible combinations of value choices for an optimized design is not feasible because it requires exorbitant calculation time. The researchers used metaheuristics, which have proven their ability to effectively deal with such a problem. In this article, we present an optimization technique based on Genetic Algorithm (GA) to address the design of the 2nd-order active low-pass filter (Rauch structure). The GA algorithm is applied to the design of the filter made with components selected from different manufactured series. CADENCE VIRTUOSO simulations are used to validate the results/performances obtained.

关键词： Resistors Metaheuristics Capacitors Low-pass filters filtering algorithms Active filters Manufacturing

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A Noise Reduction Algorithm Applied to PET Systems

A Noise Reduction Algorithm Applied to PET Systems

引用

Cross Strait Quad-Regional Radio Science and Wireless Technology Conference (CSRWTC)

作者： Xin Tang Feng Lin Yan Hu Zheng School of Electronic Science and Engineering University of Electronic Science and Technology of China China

ISBN: (数字)9798331507794

ISBN: (纸本)9798331507800

In order to achieve high-precision measurement of positron emission tomography systems, an algorithm combines empirical mode decomposition and wavelet transform is proposed to decompose and reconstruct the noisy scintillation pulse signals, and to improve the energy resolution of the system by increasing the system signal-to-noise ratio. The proposed algorithm can improve the signal-to-noise ratio from 27.65 to 36.20, which is better than RC low-pass filtering algorithm, and can improve the energy resolution of the PET system by 0.19 percentage points to 13.08%.

关键词： Wireless communication Wavelet transforms Pulse measurements Energy resolution Noise reduction Energy measurement filtering algorithms Noise measurement Positron emission tomography Signal to noise ratio

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A Genetic Algorithm and Model Order Reduction Method Based EM Topology Optimization for a Four-pole Waveguide Filter

A Genetic Algorithm and Model Order Reduction Method Based E...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Jiali Zhang Feng Feng Jin Jin Wei Liu Kaixue Ma Qi-Jun Zhang School of Microelectronics Tianjin University Tianjin China College of Physical Science and Technology Central China Normal University Wuhan China Department of Electronics Carleton University Ottawa Canada

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Electromagnetic (EM) topology optimization for waveguides is a challenging yet significant research methodology in EM optimization. A primary problem in EM topology optimization is the inefficiency of computing full-wave microwave simulations, which need to solve large system equations when altering the topology of the waveguide during optimization. In this paper, an efficient finite element method (FEM)-based EM topology optimization method, leveraging the properties of genetic algorithms (GA) and the model order reduction (MOR) method, is used to speed up the topology optimization of a four-pole waveguide filter.

关键词： Waveguide components Computational modeling Electromagnetic waveguides filtering algorithms Mathematical models Topology Microwave filters

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Environment Based Multi-Object Grasp Planning in Bin Picking Process

Environment Based Multi-Object Grasp Planning in Bin Picking...

引用

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

作者： Heng Xiong Fawu Xie Long Bai Changrong Guo Jianfeng Xu The State Key Laboratory of Intelligent Manufacturing Equipment and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China HUST-Wuxi Research Institute Wuxi China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Given the potential of bin picking to eliminate manual handing practices in manufacturing and logistics industries, this paper presents an environment-based grasp planning pipeline to address the challenges posed by complex object arrangements in cluttered environments. The pipeline integrates offline grasp planning system with online grasp filter, designed specifically for multi-object cluttered bin picking tasks. In the offline phase, the pipeline utilizes the improved Particle Swarm Optimization (PSO) algorithm to generate the grasp solutions by incorporating gripper and object models. In the online phase, the system considers grasp and robot kinematics to prioritize objects and determine the best grasp based on the grasp task scene model and the precomputed solutions from the offline phase. The proposed pipeline is validated through both simulated and experimental trials using the Franka Emika Panda robotic arm. The results demonstrate the effectiveness of the pipeline in handling multi-object cluttered bin picking tasks.

关键词： Measurement Computational modeling Robot kinematics Pipelines filtering algorithms Stability analysis Robustness Planning Manufacturing Particle swarm optimization

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An adaptive density clustering algorithm based on measurement parameters and representative points

An adaptive density clustering algorithm based on measuremen...

引用

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

作者： Yuqi Zhang Bing Wang Yuquan Chen Zhen Zhong Yang Du College of Artificial Intelligence and Automation Hohai University Nanjing China State Grid Shanghai Electric Power Research Institute Shanghai China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

DBSCAN clustering algorithm (Density-based spatial clustering of applications with noise) is a density-based spatial clustering algorithm with noise. Due to its insensitivity to noise and its ability to cluster irregularly shaped data without specifying the number of clusters , it has been widely used. However, at the same time, DBSCAN also has some problems such as excessive dependence on input parameters, cumbersome parameter adjustment and poor clustering performance for samples with uneven density. To solve the problems in DBSCAN, the MR-DBSCANS algorithm (Adaptive density clustering algorithm based on measurement parameters and representative points) is proposed according to the newly proposed parameter adaptive dynamic selection method and representative point reallocation strategy. Firstly, constructing measurement parameters based on compactness and penalty terms. Secondly, using measurement parameters to filter noise points and core points, and selecting effective neighborhood radius(Eps), the sample density judgment threshold (MinPts), and optimal neighborhood points for the core points. Then the DBSCAN redistribution strategy based on representative points is proposed, and the clustering results will be revised through the redistribution strategy, to obtain the correct clustering results. Finally, the adaptability and clustering performance of the MR-DBSCAN algorithm are simulated and verified in the dataset

关键词： Accuracy Density measurement Heuristic algorithms Noise Clustering algorithms filtering algorithms Noise measurement Artificial intelligence Robots

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PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing

PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Phillip Long Zachary Novack Taylor Berg-Kirkpatrick Julian McAuley Computer Science & Engineering UC San Diego La Jolla USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The recent explosion of generative AI-Music systems has raised numerous concerns over data copyright, licensing music from musicians, and the conflict between open-source AI and large prestige companies. Such issues highlight the need for publicly available, copyright-free musical data, in which there is a large shortage, particularly for symbolic music data. To alleviate this issue, we present PDMX: a large-scale open-source dataset of over 250K public domain MusicXML scores collected from the score-sharing forum MuseScore, making it the largest available copyright-free symbolic music dataset to our knowledge. PDMX additionally includes a wealth of both tag and user interaction metadata, allowing us to efficiently analyze the dataset and filter for high quality user-generated scores. Given the additional metadata afforded by our data collection process, we conduct multitrack music generation experiments evaluating how different representative subsets of PDMX lead to different behaviors in downstream models, and how user-rating statistics can be used as an effective measure of data quality.

关键词： filtering Data integrity Companies Signal processing filtering algorithms Data collection Explosions Multiple signal classification Speech processing Capacity planning

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An Improved Negative Selection Algorithm Based on a T Cell Multilayer Immune Tolerance Mechanism

An Improved Negative Selection Algorithm Based on a T Cell M...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Lu Peng Yiwen Liang He Yang School of Computer Science Wuhan University Wuhan China College of Information Science and Engineering Wuchang Shouyi University Wuhan China School of Computer Science Hubei University of Education Wuhan China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

The negative selection algorithm simulates part of the immune tolerance mechanism, but its simplified detection method limits its application in complex issues. This paper revisits the biological immunology and proposes a detector checking based on a multilayer immune tolerance method(DC-MIT), which focuses on the detection phase rather than the generation phase. Instead of checking directly, the detectors generated from the training set need to be activated through concentration recognition. The testing set needs to be divided into two parts: the activating set and the checking set. First, the activation set is clustered using K-means and divided into different categories. Then, the detectors that can recognize a sufficient number of abnormal samples in the same category are activated and retained through concentration activation to filter out the “bad” and non-functional detectors for this time. Finally, the checking set verifies the activated detectors. A comparison of the NSA algorithm and the classic machine learning algorithms on the Iris and Breast Cancer Wisconsin datasets shows that the method proposed in this paper is superior to other methods, especially in improving precision and false positive issues.

关键词： Training Machine learning algorithms Immunology Computational modeling Detectors filtering algorithms Nonhomogeneous media Immune system Iris recognition Testing

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Multilayer Adaptive Denoising Network for Incipient Fault Diagnosis of Gear System

Multilayer Adaptive Denoising Network for Incipient Fault Di...

引用

Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Hang Wei Chen Sun Jiafu He Liming Wang College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (数字)9798331529192

ISBN: (纸本)9798331529208

The critical issue for early fault diagnosis of gearboxes is to extract fault features as earlier as possible. However, the signal-to-noise ratio (SNR) of the early fault features is low, and they are easily buried by noise. To overcome this problem, this paper proposes a novel multilayer and multiscale adaptive denoising convolutional neural network that utilizes a multilevel filtering structure to search for the optimal filter parameters at different scales, thereby optimizing the denoising performance. The proposed method combines the principles of Wiener filtering and the idea of the multi-scale optimization, which can break through the limitation of traditional filtering and denoising algorithms that can only solve for a single filter parameter. Consequently, the proposed denoising algorithm can achieve better performance. The spalling fault experiment results demonstrate that the proposed method can effectively find the optimal filtering parameters at different scales when dealing with complex noise signals, and the denoised signals have a higher SNR.

关键词： Fault diagnosis Wiener filters Adaptive systems filtering Filter banks filtering algorithms Noise cancellation Feature extraction Convolutional neural networks Signal to noise ratio

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