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检索条件"主题词=Filtering algorithms"

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SWISP: Distributed Convoy Mining via Sliding Window-based Indexing and Sub-track Partitioning

SWISP: Distributed Convoy Mining via Sliding Window-based In...

引用

International Conference on Data Engineering

作者： Chenxu Wang Xin Yang Tianyi Li Jiaxing Wei Pinghui Wang Hongzhen Xiang Christain S. Jensen School of Software Engineering MOE Key Lab of INNS Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Department of Computer Science Aalborg University Aalborg Denmark MOE Key Lab of INNS Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the widespread deployment of location-aware mobile devices, a mass of trajectory data is being generated and collected. Mining co-movement patterns of people and vehicles from streaming and massive trajectory data has attracted much attention due to its wide applications in various fields. As a typical co-movement pattern, convoys describe objects moving together in consecutive timestamps. There are two challenges for efficient distributed convoy mining: object clustering and workload balancing. Clustering objects in each time snapshot is a time-consuming operation. In addition, on the basis of practical application scenarios, load balancing is an important consideration for distributed algorithms. To tackle the above challenges, we propose a novel method for distributed convoy mining via sliding window-based indexing and sub-track partitioning, abbreviated SWISP. We offer three major advancements. First, we develop a grid-based DBSCAN clustering algorithm named Grid-DBSCAN for distributed scenarios. It avoids the exhaustive calculation of pairwise distances for neighborhood search and thus improves computational efficiency in the clustering stage. Second, we propose a sliding window-based indexing scheme to filter out sub-tracks with less than $k$ consecutive time snapshots, significantly reducing the number of candidate sub-tracks for convoy mining. Third, we develop a distributed convoy mining algorithm based on sub-track partitioning. It exploits both temporal and spatial information of sub-tracks for data partitioning and solves the data skewness problem caused by uneven data distributions. We conduct extensive experiments on four real-world datasets. The experimental results show that our distributed algorithm can handle large-scale trajectory data and is more efficient than state-of-the-art approaches.

关键词： Clustering algorithms filtering algorithms Load management Information filters Mobile handsets Trajectory Partitioning algorithms

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Optimal Distribution Grid Scheduling Strategy in the Context of Insufficient Power Supply During Extreme Weather Events

Optimal Distribution Grid Scheduling Strategy in the Context...

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International Conference on Power and Energy Technology (ICPET)

作者： Xiaochun Yang Lixiong Xu College of Electrical Engineering Sichuan University Sichuan University Chengdu China

ISBN: (数字)9798350375855

ISBN: (纸本)9798350375862

In extreme weather conditions, there is an imbalance in the power supply and demand, leading to power shortages in the grid. Developing sound power scheduling strategies can enhance the economic efficiency of electricity usage and customer satisfaction. Therefore, addressing the issue of power rationing during inadequate grid supply, we have established an optimal load scheduling model for the grid under extreme weather conditions. This model aims to minimize power outage losses, energy storage allocation costs, and customer dissatisfaction by rational allocation of electrical energy to different types of loads in the grid, as well as adjusting the operating time of these loads. We then employed the multi-objective particle swarm optimization algorithm to solve the model and utilized the fuzzy membership degree method to filter the generated Pareto solution set to obtain the optimal solution. Subsequently, we conducted a case study using the IEEE 33-node system to validate the proposed optimization strategy. The results demonstrate that our approach effectively improves energy utilization, reduces power outage losses and storage allocation costs, and enhances customer satisfaction. Specifically, in the scenario of a 15% power shortage, the optimized value of power outage losses decreased by 36.6% compared to the random mean, and customer dissatisfaction was reduced by 59.8%.

关键词： Costs Power supplies Customer satisfaction Optimal scheduling filtering algorithms Power system reliability Resource management Particle swarm optimization Meteorology Load modeling

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A Detection Device for Quality Assessment of High-Speed Moving Filter Rods Based on Machine Vision

A Detection Device for Quality Assessment of High-Speed Movi...

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

作者： Yi Bao Wanzhu Feng Chuan Yang Gangxing Ma Yulin Huang Department of Electrical Engineering University of Electronic Science and Technology of China (UESTC) Chengdu China Sichuan Sanlian New Material Company Ltd. Chengdu China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

This study builds an online detection device for quality assessment of high-speed moving filter rods based on machine vision. This detection device effectively addresses the problem of high defect rate associated with filter rods high-speed forming machines. Three basic components make up the device: a high-speed imaging system, a control and detecting system, and an electromechanical support system. The high-speed imaging system takes pictures of filter rods using a high-speed line array camera. Once the image is framed, the data is transferred to the industrial computer's image acquisition card via the camera link. When the industrial computer obtains the image data, it processes the images with machine vision methods and outputs the processing results to the PLC controller, which generates instructions for discarding defective filter rods based on the output detection results. The results indicate that the high-speed online detection device for filter rod quality based on machine vision effectively resolves quality issues such as excess capsules, missing capsules, mixed capsules, and capsules position shift during the high-speed production of filter rods.

关键词： Accuracy Machine vision Process control Production filtering algorithms Information filters Control systems Real-time systems Quality assessment Reliability

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A Hybrid Feature-Selection Method Based on mRMR and Binary Peacock Optimization Algorithm

A Hybrid Feature-Selection Method Based on mRMR and Binary P...

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International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Bibhuprasad Sahu Amrutanshu Panigrahi Abhilash Pati Rajalaxmi Mishra Sudhir Kumar Senapati Kashyapa Kanti Sahu Department of Information Technology Vardhaman College of Engineering (Autonomous) Hyderabad India Department of CSE Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha India Department of CSA College of IT and Management Education Bhubaneswar Odisha India Department of Computer Science Pj College of Management and Technology Bhubaneswar Odisha India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

An essential aspect of developing effective biomarkers that facilitate disease diagnosis and drug discovery is identifying the most critical characteristics from microarray data. Since it is expensive to validate biomarkers, the number of biomarkers must be reduced while retaining their effectiveness. A common challenge with microarray data is that it results in a large number of properties, known as the curse of dimensionality. Almost all the feature selection (FS) techniques developed up-to-date do not possess the ability to result in lower dimensionality, ensure sound classification, easy algorithms and exhaustive search of the parameter space. This study describes a hybrid FS approach by combining the mRMR and binary variant of the peacock algorithm (Binary POA) algorithm. It introduces mRMR by modifying its quantization functions to the continuous nature of the microarray data attribute and uses mRMR as a first step to normalize both datasets and select an initial feature subset. In the following step, a binary POA algorithm is deployed to optimize the selected feature subset. The performance of the proposed mRMR-BPOA ML model is evaluated using benchmark classifier Support Vector Machine. The Result analysis proves that the performance of mRMR-BPOA is superior to the existing counterparts.

关键词： Support vector machines Quantization (signal) Biomarkers filtering algorithms Feature extraction Classification algorithms Drug discovery Medical diagnosis Optimization Cancer

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A Novel Dolph-Chebyshev Filter for Prediction of Huntington’s Disease

A Novel Dolph-Chebyshev Filter for Prediction of Huntington...

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Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), International Conference on

作者： Lopamudra Das Judhisthir Dash Sarita Nanda J.K Das Suprava Patnaik Dept. of Electronics Engineering Silicon University Bhubaneswar India Dept. of Electronics Engg Silicon University Bhubaneswar India School of Electronics Engineering KIIT Bhubaneswar India

ISBN: (数字)9798350369175

ISBN: (纸本)9798350369182

Mutation in all the cases doesn’t specify cancer; rather a special type of mutation, where a trinucleotide repeat (cytosine-adenine-guanine (CAG)) in DNA exceeds the normal range, causes Huntington’s disease (HD); a mutational disorder. The present work specifies a non-invasive method of spectral estimation technique based on Trigonometric mapping. A Fourier Transform approach along with the Dolph-Chebyshev filter has been designed and applied to raw genomic data for predicting the disease. Moreover, the proposed algorithm has been tested for both healthy and HD benchmark datasets. Since a disease gene is more random compared to a healthy one, the variance is calculated. It is observed that in the case of all HD datasets, the value of $\operatorname{SNR}(\mu / \sigma)$ is less than 0.1, whereas, in the case of a healthy gene set, it is found to be more than 0.1. For all other parameters also the result is found to be satisfactory.

关键词： Technological innovation Fourier transforms Genomics Estimation filtering algorithms Prediction algorithms Information filters Bioinformatics Diseases Dementia

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Comparative Study of Convolutional Neural Network and Particle Smoother-Based Expectation Maximization for Estimation of 82Rb Myocardial Perfusion PET Kinetic Parameters

Comparative Study of Convolutional Neural Network and Partic...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： M. Chin S. J. Zou G. Chinn C. S. Levin Electrical Engineering Stanford University Stanford California United States of America Radiology Stanford University Stanford California United States of America

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

In positron emission tomography (PET), dynamic PET scans, which record a series of time frames, can be paired with compartmental models to estimate kinetic parameters and enhance disease characterization. In our research, we introduced two new methods for PET kinetic analysis: one uses a particle smoother in an Expectation-Maximization (PSEM) framework, and the other employs a convolutional neural network (CNN). We validated these methods by applying them to simulated 82Rb dynamic PET myocardial perfusion studies and comparing their performance with traditional nonlinear least squares (NLLS) methods and a Kalman filter-based Expectation-Maximization (KEM) algorithm. The results show that the success rate, defined as the number of cases in which the proposed method outperforms the NLLS algorithm over the total number of simulations, for parameters F, k 3 , and k 4 were 46.0%, 67.5%, and 54.0% for PSEM and 86.5%, 83.0%, and 79.5% for the CNN model.

关键词： Microwave integrated circuits Semiconductor detectors Heuristic algorithms Estimation Myocardium filtering algorithms Kinetic theory

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Improved Resolutions in Forward Looking MIMO-SAR through Burg Algorithm

Improved Resolutions in Forward Looking MIMO-SAR through Bur...

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European Radar Conference (EURAD)

作者： Muge Bekar Ali Bekar Chris Baker Marina Gashinova Microwave Integrated Systems Laboratory (MISL) University of Birmingham UK

ISBN: (数字)9782874870798

ISBN: (纸本)9798350385137

Attaining high angular resolutions through compact designs is a preferred approach for vehicles with increasingly dense packaging, aiming to achieve higher levels of automation. Hence, the motion of the MIMO radar platform has been exploited to obtain higher cross-range resolution in off-boresight through forward-looking (FL) MIMO-SAR, but no improvements have been made in boresight which is the most crucial direction for path planning. In this paper, we propose use of Burg algorithm (BA) with FL MIMO-SAR technique to enhance the cross-range resolution in boresight and to achieve further enhanced the resolution off-boresight. Here backprojection (BP) is used to obtain SAR images. BA is applied either before or after BP image formation and the performance of both methods is compared based on the analysis of achieved resolution, background level and computational resources required. The proposed methods are validated through both simulation and real-world experiment conducted at 77 GHz.

关键词： Image resolution MIMO radar Imaging Radar Radar imaging filtering algorithms Packaging Radar polarimetry Path planning Image filtering

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Towards Efficient Ridesharing via Order-Vehicle Pre-Matching Using Attention Mechanism

Towards Efficient Ridesharing via Order-Vehicle Pre-Matching...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhidan Liu Jinye Lin Zhiyu Xia Chao Chen Kaishun Wu Hong Kong University of Science and Technology (Guangzhou) Shenzhen University Chongqing University

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Dynamic ridesharing has garnered significant attention in recent years due to its numerous benefits. Existing ridesharing algorithms often employ a “filter-and-refine” frame-work, where a large set of candidate vehicles is initially selected for each ride order, followed by computationally intensive route planning for each candidate. However, this process can lead to significant response delays and limit system efficiency. To address this challenge, we propose an order-vehicle pre-matching recommendation approach (PreMR) that refines the candidate set before route planning. PreMR leverages spatial-temporal intervals and a self-attention mechanism to encode diverse order and vehicle information into uniform and informative representations, enabling it to accurately identify the most suitable vehicles for each order. Extensive experiments using real-world datasets and four representative ridesharing algorithms demonstrate that PreMR significantly reduces order response time (by 46.78% on average) while maintaining high service quality, with a slight trade-off in the order completion rate.

关键词： Shared transport Heuristic algorithms Scalability filtering algorithms Planning Delays Time factors Data mining Vehicle dynamics Pattern matching

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Efficient High Degree Continuous-Time Cubature Kalman Filters

Efficient High Degree Continuous-Time Cubature Kalman Filter...

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IEEE National Conference on Radar

作者： Benjamin Davis Hill Technical Solutions Huntsville AL USA

ISBN: (数字)9798350329209

ISBN: (纸本)9798350329216

Sigma-point filters, including the Unscented Kalman Filter (UKF) and the Cubature Kalman Filter (CKF) are important modern non-linear estimation algorithms. The standard forms of these algorithms are applied to discrete-time systems, however, continuous-time forms also exist. In previous work, the author developed a runtime optimization to sigma-point filters in discrete time called the Hybrid Sigma-point Extended Kalman Filter (HESKF). Using this technique, sigma-point integration is applied to “difficult” subspaces of the dynamics, whereas “easy” subspaces are treated with Jacobian linearization, resulting in a blended filter algorithm. This present work extends the HESKF technique to a continuous-time dynamics and demonstrates the improvement in runtime with an appropriate example.

关键词： Jacobian matrices Discrete-time systems Runtime Heuristic algorithms Estimation Radar filtering algorithms

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Finite-Time Fault Detection for Nonlinear Delayed Systems With Deception Attacks Under Dynamic Event-Triggered Mechanism

Finite-Time Fault Detection for Nonlinear Delayed Systems Wi...

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

作者： Xue Zhao Siteng Ma Yue Zhao Tiantian Hu Zhihui Wu Department of Applied Mathematics Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper tackles the finite-time fault detection (FD) issue for a class of nonlinear systems with deception attacks and mixed time-delays including probability interval delays and distributed delays. For the purpose of alleviating the communication burden, a dynamic event-triggered mechanism (DETM) is introduced to arrange data transmission. The sufficient criteria are obtained to ensure that the FD system can achieve satisfactory performance. On this basis, the parameters of FD filter are obtained by the linear matrix inequality (LMI) method. At last, a simulation is provided to verify the effectiveness of proposed FD algorithm.

关键词： Sufficient conditions Maximum likelihood detection Fault detection Stability criteria Nonlinear filters filtering algorithms filtering theory

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