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检索条件"主题词=Filtering algorithms"

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Lattice-Based Decomposition of Transitive Homogeneous Class Hierarchies*

Lattice-Based Decomposition of Transitive Homogeneous Class ...

引用

IEEE International Conference on Intelligent Computer Communication and Processing (ICCP)

作者： Dmytro O. Terletskyi Department of Methods and Technological Tools for Creating Intelligent Software Systems V. M. Glushkov Institute of Cybernetics of NAS of Ukraine Kyiv Ukraine

ISBN: (数字)9798331539979

ISBN: (纸本)9798331539986

The decomposition of conceptual hierarchies is a significant task in knowledge engineering and software engineering, allowing the extraction of hidden or non-obvious knowledge pieces, or software subsystems, and the relations between them. During the last decades, many different approaches to the analysis and decomposition of conceptual hierarchies were developed, however, most of them do not pay enough attention to the semantic consistency of the decomposition, considering the internal semantic dependencies between the class attributes. Consequently, extracted knowledge items and software subsystems, can be semantically inconsistent due to the contradiction with such dependencies and model unrealistic or impossible entities or subsystems within a particular domain. Therefore, we propose a new approach to conceptual hierarchy decomposition based on the internal semantic dependencies between the properties and methods of the hierarchy members. Implementing the approach, an appropriate algorithm was developed for the decomposition of class hierarchies within the object-oriented dynamic networks, that constructs only semantically consistent subclasses and sub hierarchies. To demonstrate the main scenarios of algorithm application, we provided a comprehensive example of the decomposition of conceptual hierarchy, consisting of homogeneous classes of objects.

关键词： Knowledge engineering filtering Heuristic algorithms Object oriented modeling Semantics Software algorithms Lattices filtering algorithms Software Software engineering

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Enhanced SVD Denoising Method Based on Hankel Matrix Decomposition for Seismic Data

Enhanced SVD Denoising Method Based on Hankel Matrix Decompo...

引用

Image Processing and Computer Applications (ICIPCA), IEEE International Conference on

作者： Honglin Dai Yucheng Tang Xiangyu Han Weiming Xiong Faculty of Business Administration China University of Petroleum-Beijing at Karamay Karamay City China Petroleum Institute China University of Petroleum-Beijing at Karamay Karamay City China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

This study introduces an improved SVD denoising method based on the decomposition of the Hankel matrix. This method denoises by analyzing the singular value spectrum of the Hankel matrix, where larger singular values typically represent components with higher coherence in the data, and smaller singular values correspond to less coherent parts. By selecting different rank reduction parameters based on the characteristics of the processed data, this method achieves noise reduction and enhances SNR. Subsequently, a two-dimensional seismic data model is constructed to validate this algorithm. The key to this algorithm is determining the truncation position of the singular value matrix (rank reduction parameter), and the optimal rank reduction parameter is efficiently found using the energy ratio. Practical case studies have shown a significant improvement in the SNR of incoherent data, effectively suppressing random noise and improving the denoising effect for non-horizontal coherent axes. Overall, the Hankel matrix construction method effectively suppresses random noise while maintaining high fidelity of the signal data.

关键词： Transmission line matrix methods Noise Noise reduction Signal processing algorithms filtering algorithms Matrix decomposition Noise measurement Velocity measurement Signal to noise ratio Testing

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Financial Fraud Defense Strategy based on Gradient Compensated Asynchronous Federated Learning

Financial Fraud Defense Strategy based on Gradient Compensat...

引用

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Tongrui Liu Yizhi Zhou Zhipeng Song Xibei Jia Shan Wu Heng Qi School of Computer Science and Technology Dalian University of Technology Dalian China Shenzhen Audaque Data Technology Co. Ltd. Shenzhen China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Asynchronous federated learning (AFL) allows participants to immediately submit trained models without waiting for other participants, building upon the federated learning (FL). Due to privacy concerns, FL is more susceptible to financial fraud, compounded by the gradient delay issues introduced by asynchronous submissions, making defense against financial fraud more challenging. Motivated by the above finding, we propose a secure and privacy-preserving AFL defense method for image datasets with an implanted backdoor via filtering redundant neurons (BDAFL), enhancing its resilience against such attacks without compromising privacy. We utilize gradient compensation to mitigate the impact of delays introduced by asynchrony. To counter financial fraud, we employ an anomaly detection algorithm based on neurons’ weights and ensemble distillation to eliminate the affected neurons implanted with the backdoor, rendering the attack ineffective. Extensive experiments demonstrate the effectiveness and superiority of our approach.

关键词： Privacy Federated learning Neurons Finance filtering algorithms Rendering (computer graphics) Robustness Fraud Delays Resilience

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Deep Learning-Based Pulse-Shaping Filter Estimation for Fine-Grained WiFi Sensing

Deep Learning-Based Pulse-Shaping Filter Estimation for Fine...

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IEEE International Conference on Communications (ICC)

作者： Han Hu Ruiqi Kong Ke Xu He Henry Chen Department of Information Engineering The Chinese University of Hong Kong Hong Kong Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In numerous WiFi sensing applications, such as passive human localization, the precision of sensing is often influenced by the estimation accuracy of multipath parameters. Several existing algorithms leverage pulse-shaping filter information to enhance multipath and channel estimation. However, WiFi chips do not disclose this filter information, and no current research has focused on measuring or estimating these pulse-shaping filters. In this paper, we introduce a new deep learning approach for the accurate estimation of pulse-shaping filters using channel state information (CSI), which incorporates both multipath channel information and pulse-shaping filter information. Specifically, we construct a convolutional neural network consisting of an encoder-regressor architecture, where the encoder translates the CSI into a latent representation, and the regressor subsequently estimates the pulse-shaping filter from this representation. Our proposed model's efficacy is demonstrated through its low normalized root mean squared error (NRMSE) in a variety of channel conditions, highlighting its ability to accurately estimate pulse-shaping filters.

关键词： Wireless communication Semiconductor device measurement Accuracy Estimation Channel estimation filtering algorithms Information filters

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Power Control Method for Composite Energy Storage Systems Utilizing Wavelet Analysis

Power Control Method for Composite Energy Storage Systems Ut...

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China International Electrical and Energy Conference (CIEEC)

作者： Xusheng Wang Jian Cui Li Ma Xu Wang Yan Jia North China Branch of China Petroleum Engineering Construction Co. Ltd CPECC Hebei China Inner Mongolia University of Technology College of Energy and Power Engineering IMUT Hohhot China Key Laboratory of Wind Energy and Solar Energy Technology Ministry of Education of China IMUT Inner Mongolia University of Technology College of Energy and Power Engineering Hohhot China

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Due to the stochastic, intermittent, and fluctuating nature of wind speed variations, power fluctuations occur in wind power generation systems. Energy storage technology plays a crucial complementary role in renewable energy generation by mitigating these fluctuations. This paper focuses on the study of short-term output power fluctuations in the system. Firstly, based on the principles of a first-order low-pass filtering algorithm, the paper calculates, analyzes, and discusses the smoothing effects of different cutoff frequencies on wind power, while meeting the goal of smoothing power fluctuations in wind power generation systems. The cutoff frequency of the first-order low-pass filtering algorithm is determined to be 0.3. Secondly, through comparative analysis of reconstruction errors, the paper determines that the wavelet decomposition level used is 6. Based on the power characteristics of energy storage components, signals of different fluctuation frequencies are allocated to lithium iron phosphate batteries and supercapacitors for smoothing via control of power electronic devices. Thirdly, the paper optimizes the power allocation method for compound energy storage systems, considering the state of charge, and employs a fuzzy PI control method to control the charging and discharging of the energy storage system. Compared to traditional PI control methods, the working state of the energy storage devices is more stable, with a decrease of 1.87% in SOC fluctuation for lithium iron phosphate batteries and a decrease of 0.64 % for supercapacitors. This protects the use of energy storage equipment, and simulation validates the feasibility of this method.

关键词： Fluctuations Cutoff frequency Wind power generation Supercapacitors filtering algorithms Lithium Wavelet analysis

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Improving Alerting in the Monitoring System Using Machine Learning algorithms

Improving Alerting in the Monitoring System Using Machine Le...

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International Conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Mykhailo Klymash Serhii Zablotskyi Vasyl Pohranychnyi Institute of Telecommunications Radioelectronics and Electronic Engineering Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

The paper discusses the methods for building monitoring systems. It analyses standard methods for detecting and responding with alert systems based on given values of time series metrics. A method for improving the alert system by integrating artificial intelligence is also proposed. It will help filter out false and system alerts. Additionally, using AI analysis, the system administrator will receive suggested solutions or improvements in specific network segments.

关键词： Training Machine learning algorithms Power supplies Time series analysis Systems architecture filtering algorithms Turning Telecommunications Monitoring Standards

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Mining CFD Algorithm on Medium Dataset Using Queue

Mining CFD Algorithm on Medium Dataset Using Queue

引用

Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Purva Baghel Poorva Pohekar Suhani Gahukar Latika Pinjarkar Poorva Agrawal Gagandeep Kaur Symbiosis Institute of Technology Nagpur campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

Database and Data Mining is an important domain in the IT Sector as maintenance of big data leads to an increase in the accuracy and efficiency of projects working on the basis of the same datasets. In this research paper, the main aspect of data mining is discussed i.e., effective algorithms used for data mining and data analysis. We have discussed the algorithm for data mining CFD (Conditional Functional Dependency). Our goal is to find high-quality CFD relation sets. We have applied CFD on our college’s medium data to filter the dirty items. In this experiment, we used a standard CFD and data structure like a queue to clean our data. We have selected the data of our college to clean up the data of the students who have left or didn’t pass the degree. We evaluate our set of CFD by comparing them with the number of students enrolled and the number of degrees allotted to the students. We have also given examples on a small-scale basis about the above techniques and methods.

关键词： Machine learning algorithms Databases Machine learning filtering algorithms Data structures Maintenance Data mining Indexes Time complexity Standards

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Research on Substation Facility Segmentation Based on the SoftGroup Network

Research on Substation Facility Segmentation Based on the So...

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Clean Energy and Electric Power Engineering (ICCEPE), International Conference on

作者： Yun Chen Duanjiao Li Ying Zhang Jianming Liu Yongchao Liang Guangdong Power Grid Co. Ltd. Guangzhou Guangdong China

ISBN: (数字)9798350390315

ISBN: (纸本)9798350390322

To address the problem of fine three-dimensional reconstruction in intelligent substation scenes, we propose a segmentation algorithm for substation equipment point clouds. For the main structure of the substation, we first use the cloth simulation filter (CSF) algorithm to remove ground points, followed by a region-growing algorithm based on random sample consensus (RANSAC) line fitting to eliminate non-substation facility points. Finally, we segment the substation equipment point cloud based on the SoftGroup network, obtaining instance and semantic segmentation results for each equipment point cloud. Experimental results demonstrate that our method can accurately identify the 22 subdivided substation equipment, which provides a technical foundation for the subsequent construction of three-dimensional models for intelligent substations.

关键词： Point cloud compression Training Solid modeling Substations Laser radar Semantic segmentation Software algorithms filtering algorithms Software Testing

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Comparative Analysis of Feature Selection Methods Using Swarm-Based Optimization Techniques

Comparative Analysis of Feature Selection Methods Using Swar...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Ashish Kumar Singh Anoj Kumar Ashish Ranjan Mishra Dept. of Computer Sci. & Engineering MNNIT Allahabad Prayagraj India Dept. of Computer Sci. & Engineering Rajkiya Engineering College Sonbhadra India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

Feature selection is a vital data preprocessing technique aimed at selecting informative features from a dataset, which is essential for improving classification performance. Optimization involves deriving an optimal solution from feasible options, often drawing inspiration from nature. Particle swarm optimization (PSO) is highlighted as an effective metaheuristic algorithm for feature selection, capable of achieving satisfactory results with reduced computational complexity. Techniques are categorized into evolutionary, swarm intelligence, physics-based, and human-based approaches, with swarm intelligence particularly notable for its emergent intelligent behavior from simple agents. Swarm intelligence gains traction due to its speed, adaptability, and self-organization. These optimization techniques are highly efficient for tasks like optimal feature selection. In datasets of high dimension, quantity of noisy, irrelevant, and redundant features degrade clustering performance, necessitating methods to maximize accuracy, accelerate output, and filter out irrelevant features.

关键词： Surveys Dimensionality reduction Accuracy Metaheuristics Nearest neighbor methods filtering algorithms Feature extraction Noise measurement Particle swarm optimization Unsupervised learning

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Robust Visual-Inertial-Wheel SLAM for Ground Robots in Complicated Scenes

Robust Visual-Inertial-Wheel SLAM for Ground Robots in Compl...

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

作者： Xianzhe Yuan Dingxin He Xiong Peng Zhi-Wei Liu School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

Simultaneous Localization and Mapping (SLAM) in real-world scenes faces several challenges, including highly dynamic people and vehicles which make the static assumption of traditional SLAM no longer valid, and some indoor dim scenes which cause the loss of visual features. This paper presents a robust visual-inertial-wheel SLAM algorithm for ground robots that can handle these challenges in complicated environments. The proposed algorithm introduces a lightweight object detection network as the front-end to remove dynamic features in real time, and develops a novel filter-based approach to enhance feature tracking. The back-end of the proposed algorithm is modeled as an optimization-based bundle adjustment problem which includes constraints from IMU, wheel, visual and ground measurement. The proposed method is extensively evaluated on OpenLORIS dataset that includes real-world complicated scenes to validate its effectiveness.

关键词： Bundle adjustment Visualization Simultaneous localization and mapping Heuristic algorithms Wheels Object detection filtering algorithms

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