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检索条件"主题词=Filtering algorithms"

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Customer Segmentation in Steel Sales : Optimizing Profitability with RFM Model and K-Means Algorithm

Customer Segmentation in Steel Sales : Optimizing Profitabil...

引用

Artificial Intelligence, Blockchain, Cloud Computing, and Data Analytics (ICoABCD), International Conference on

作者： Mera Kartika Delimayanti Anita Hidayati Dewi Kurniawati Nadya Zahra Rahmadani Mas Brillianesa Faydhurrahman Department of Computer and Informatics Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798350368451

ISBN: (纸本)9798350368468

This paper is on customer segmentation in steel sales, whose objective is to improve profitability and customer relationship management. The current research categorizes customers based on purchasing behaviors by using the RFM model and the K-Means clustering algorithm. Due to this approach, the company will be able to formulate stratified marketing plans for the requirements and Preferences of various customer segments identified and hence ensure high retention and satisfaction. The RFM model assesses the three main customer behaviors of purchase recency, purchase frequency, and monetary value. This gives a set of metrics that provide the overall depth of customer engagement, which is important in segmentation. Then, K-Means is applied to cluster these similar patterns of customer purchasing, to precisely target market customers. The goodness of the segmentation strategy is measured through metrics such as the Elbow Method, Davies-Bouldin Index, and Silhouette Coefficient, which provide proper cluster selection and meaningful segmentation. This eventual outcome has proved that this combined approach will maximize the revenue potential and enhance customer satisfaction tremendously through personalized marketing efforts. Eventually, the embedding of the RFM model within K-Means clustering offers a robust framework for strategic decision-making and realizing competitive advantage within the steel industry.

关键词： Measurement Profitability Clustering algorithms Steel industry filtering algorithms Data models Steel Indexes Elbow Standards

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Optimization Study of Wireless Sensor Positioning Based on Improved MCM Algorithm

Optimization Study of Wireless Sensor Positioning Based on I...

引用

Computer Communication, Networks and Information Science (CCNIS), International Conference on

作者： Deyang Lin Zhenrui Zhang Hongpeng Sun Wenting Ma College of Data Science and Information Engineering Harbin Huade University Harbin Heilongjiang China

ISBN: (数字)9798331507046

ISBN: (纸本)9798331507053

With the rapid development of wireless sensor network, people need the positioning accuracy and use cost of wireless sensor nodes. To solve this problem, the wireless sensor positioning method to improve the fitness function of particle swarm algorithm, and adopts the cross variation processing to improve the sample filtering condition. The improved algorithm uses collinearity to filter the anchor nodes and use the mean offset for weight assignment. Experiments show that the localization error of the improved Monte Carlo algorithm based on particle swarm is 9.2% and 5.8% lower than the other algorithms when the number of anchor nodes is 20, and 15.2% and 11.4% lower for the sample number is 100, and the communication cost is 0.27 higher than the anchor node density of 3. Therefore, the improved Monte Carlo algorithm can effectively improve the positioning accuracy of the wireless sensor nodes, and compared with other algorithms, the use cost of the improved algorithm is less increased. This study provides a reference for node screening for the subsequent optimization of wireless sensor positioning. Key words: wireless sensor; Monte Carlo; mean offset; collinearity; localization optimization

关键词： Location awareness Wireless communication Wireless sensor networks Information science Monte Carlo methods Accuracy Costs filtering algorithms Particle swarm optimization Optimization

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A Decoupled Channel Estimation Method for Beyond Diagonal RIS

A Decoupled Channel Estimation Method for Beyond Diagonal RI...

引用

Asilomar Conference on Signals, Systems & Computers

作者： Bruno Sokal Fazal-E-Asim André L. F. de Almeida Hongyu Li Bruno Clerckx Federal University of Ceara Fortaleza Brazil Imperial College London London U.K.

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Beyond diagonal reconfigurable intelligent surface (BD- RIS) is a new architecture for RIS where elements are interconnected to provide more wave manipulation flexibility than traditional single-connected RIS, enhancing data rate and coverage. However, channel estimation for BD- RIS is challenging due to the more complex multiple-connection structure involving their scattering elements. To address this issue, this paper proposes a decoupled channel estimation method for BD- RIS that yields separate estimates of the involved channels to enhance the accuracy of the overall combined channel by capitalizing on its Kronecker structure. Starting from a least squares estimate of the combined channel and by properly reshaping the resulting filtered signal, the proposed algorithm resorts to a Khatri-Rao Factorization (KRF) method that teases out the individual channels based on simple rank-one matrix approximation steps. Numerical results show that the proposed decoupled channel estimation yields more accurate channel estimates than the classical least squares scheme.

关键词： Training Computers Accuracy Channel estimation Scattering Estimation Computer architecture Reconfigurable intelligent surfaces filtering algorithms Approximation algorithms

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A Point Cloud Registration Method for Antarctic Power Cabins Based on an Improved SAC-IA Algorithm

A Point Cloud Registration Method for Antarctic Power Cabins...

引用

Machine Learning and Computer Application (ICMLCA), International Conference on

作者： Junyao Lu Shuo Shan Kanjian Zhang Haikun Wei School of Automation Southeast University Nanjing China

ISBN: (数字)9798331530334

ISBN: (纸本)9798331530341

This paper presents a point cloud registration method for Antarctic power cabins based on an improved SAC-IA algorithm. To address the issues of measurement errors in structured light scanning and edge structure damage caused by traditional mean depth filtering, as well as inaccurate random point pair selection and long computation times in the SAC-IA algorithm, the following work was conducted: First, an improved depth filtering algorithm was designed to better filter out depth noise while preserving edge information. Second, ISS keypoint detection and cosine similarity were used to match the best feature pairs, combined with the RANSAC method to remove incorrect matches. Experimental results show that the improved algorithm significantly optimizes the number of key points and matching pairs, greatly saves computational resources, and enhances registration accuracy and robustness.

关键词： Point cloud compression Matched filters Measurement errors Accuracy Machine learning algorithms Noise Machine learning filtering algorithms Antarctica Robustness

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A Study on Banana Leaf Disease Detection Based on Grabcut Algorithm Using Multispectral Camera

A Study on Banana Leaf Disease Detection Based on Grabcut Al...

引用

International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： N. Jeebaratnam Chinmayee Dora Dhawaleswar Rao CH Electronics and Communication Engineering Centurion University of Technology and Management Paralakhemundi India Computer Science Engineering Centurion University of Technology and Management Paralakhemundi India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

The majority of people in India rely on farming as their primary source of income. Farmers encounter a variety of difficulties as a result of climate change. Decrease in yield is due to diseases on the plant. The quickest and easiest way must be used to identify the illness. Machine learning and image processing techniques are mostly used to automatically identify the diseases. In this article examined a method for diagnosing diseases in banana plants, structure, Pigment and surface. In this paper, a novel Henry Gas based Grab Cut (HGGC) algorithm by the integration of the Henry Gas Solubility Optimization algorithm (HGSO) and Grab Cut (GC) algorithm to detect the disease in the banana plant using multi spectral camera. Initially, the pre-processing is done by using a median filter for the removal of noises in the image. The segmentation is carried out using a region-based edge normalization after the images have been pre-processed using a median filter. Here, a new integrated system is developed for feature extraction combining Convolutional Neural Networks (CNN). In order to improve disease diagnostic accuracy, a region-based convolution neural network is utilized to detect the disease area by extracting and classifying information. Additionally, CNN is used to retrieve relevant attributes from the raw intake photographs so that the DL models can be trained. Consequently, the proposed approach is implemented in the PHYTHON framework. The efficiency of the developed HGGC is then demonstrated using tables and graphs by comparing the performance measures with existing methods. Accordingly, the proposed model achieved a better result by utilizing accuracy, sensitivity, specificity and false detection rate when compared to the existing methods.

关键词： Plant diseases Accuracy Signal processing algorithms filtering algorithms Feature extraction Cameras Convolutional neural networks Data mining Optimization Surface treatment

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Study of Quasi-Z-Source Three-Phase Four-Switch Matrix Converter Under Unbalanced Input

Study of Quasi-Z-Source Three-Phase Four-Switch Matrix Conve...

引用

IEEE Region 10 International Conference TENCON

作者： Yougui Guo Haowei Xiang College of Automation and Electronic Information Xiangtan University Xiangtan China

ISBN: (数字)9798350350821

ISBN: (纸本)9798350350838

Aiming at the control problem of a new topology of Quasi-Z source three-phase four-leg direct matrix converter (TFDMC), a new method is proposed to solve the problem under unbalanced input conditions. Based on the idea of direct current control on the grid side, the combined current predictive control of the grid side current and the output of the matrix converter is carried out, which simplifies the complicated algorithm of the traditional filter. Based on the principle of minimizing reactive power and controlling the fluctuation of active power, the phase sequence of the current on the grid side is separated, so as to carry out the model predictive control. This method not only improves the output current quality of the matrix converter, but also improves the current quality of the grid side. simulink simulation verifies its effectiveness.

关键词： Current control Reactive power Fluctuations Predictive models filtering algorithms Prediction algorithms Topology Matrix converters Predictive control IEEE Regions

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Multi-Feature Recognition Algorithm of ECG Signal Based on Adaptive Threshold Back Detection

Multi-Feature Recognition Algorithm of ECG Signal Based on A...

引用

IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems

作者： Jingyu Zhao Qinran Zhang Sikai Wang Li Zhang Guoxin Pan Peng Su School of Mechanical and Electrical Engineering Beijing Information Science and Technology University Beijing China Rehabilitation Hospital National Research Center for Rehabilitation Technical Aids Beijing China Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability and Key Laboratory of Neuro-functional Information and Rehabilitation Engineering of the Ministry of Civil Affairs National Research Center for Rehabilitation Technical Aids Beijing China Master of engineering Senior engineer. Mainly engaged in rehabilitation engineering and machine design

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

External counterpulsation is a physical method for treating ischemic diseases by triggering sequential compression of the lower limbs of the human body by the R-wave of ECG signal. Therefore, the performance of ECG signal feature recognition algorithm affects the effectiveness of the treatment. This paper presents a multi-feature recognition algorithm aimed at preventing missed and false detections. Firstly, a dual-threshold differentiation algorithm is used to identify the S-wave in the preprocessed ECG signal. Based on an adaptive S-R peak value threshold, a back-detection algorithm is conducted on the R-waves within the same cycle, enabling R-wave detection. An adaptive window is built on the basis of R-wave, and the T and P wave in the window are detected according to the first-order difference threshold. Finally, the algorithm was validated by using MIT-BIH and clinical ECG signal. The accuracy of R and T wave reached 99.9% and 99.85% Respectively. This represents a significant improvement over traditional algorithms and effectively prevents missed and false detections. It meets the requirements for clinical application of rehabilitation equipment.

关键词： Accuracy Sensitivity Databases Signal processing algorithms Electrocardiography Signal processing filtering algorithms Feature extraction Control systems Intelligent systems

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Estimation of Vehicle Sideslip Angle and Tire Cornering Stiffness Considering Mass Mismatch

Estimation of Vehicle Sideslip Angle and Tire Cornering Stif...

引用

Conference on Vehicle Control and Intelligence (CVCI)

作者： Zhongtao Wu Lilin Shen Shuo Bai Fengwei Dong Guodong Yin School of Mechanical Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331504892

ISBN: (纸本)9798331504908

As vehicles are utilized for transportation, their actual load is changeable, and the total mass is not remaining constant. Mismatch in mass parameters can lead to inaccuracies in the vehicle model, thereby diminishing the estimation accuracy of crucial state parameters like tire cornering stiffness (TCS) and sideslip angle (SA). To tackle these issues, this research proposes a co-estimation algorithm for the front and rear TCS and SA of vehicles based on Strong Tracking Unscented Kalman Filter (STUKF). Integrating the strong tracking concept into the Unscented Kalman Filter (UKF) by introducing a fading factor matrix, and real time adjustment of state error covariance matrix based on residual sequence orthogonality criterion to achieve swift correction of state estimation vector. To assess the efficacy of the STUKF algorithm under different road conditions, this research select different road friction-coefficients and compare the results with those obtained from EKF and RLS algorithms. The findings illustrate that the STUKF algorithm has enhanced the accuracy of estimation and demonstrates strong anti-interference capabilities for different road conditions and strong robustness to state mutations.

关键词： Accuracy Roads Transportation filtering algorithms Tires Robustness Vectors Kalman filters Covariance matrices State estimation

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Distributed Diffusion-Based Manifold Particle Filters for Orientation Estimation

Distributed Diffusion-Based Manifold Particle Filters for Or...

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European Signal Processing Conference (EUSIPCO)

作者： Claudio J. Bordin Marcelo G.S. Bruno Universidade Federal do ABC Santo André SP Brazil Instituto Tecnológico de Aeronáutica São José dos Campos SP Brazil

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Determining orientation is a fundamental problem in fields such as robotics, computer vision, and autonomous driving. In three-dimensional space, orientations can be represented in many forms, including unit quaternions or points of the Special Orthogonal Group (SO(3)). These representations present challenges due to their non-Euclidean nature. This paper addresses the problem of computing a joint Bayesian estimate of a rotation given the observations of a set of receivers that consist of corrupted and transformed versions of the true orientation. We evaluate the performance of the described algorithms via numerical simulations in terms of accuracy and communication requirements.

关键词： Manifolds Quaternions Signal processing algorithms Europe Estimation Receivers filtering algorithms Numerical simulation Particle filters Numerical models

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Clock Bias Prediction by Application of Variational Bayesian Adaptive Kalman Filter

Clock Bias Prediction by Application of Variational Bayesian...

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European Frequency and Time Forum (EFTF)

作者： Dehao Chen Hang Yi Dandan Li Ang Li Huijun Yang Xueyun Wang Science and Technology on Metrology and Calibration Laboratory Beijing Institute of Radio Metrology and Measurement Beijing China

ISBN: (数字)9798350354270

ISBN: (纸本)9798350354287

To improve the performance of traditional Kalman filtering algorithms in clock bias prediction., this paper proposes the application of variational Bayesian adaptive Kalman filtering algorithm to achieve clock bias prediction. This method addresses the shortcomings of traditional Kalman filters that require accurate observation noise models and system noise models in effective application. It proposes a combined algorithm of Sage Husa adaptive Kalman filtering algorithm and variational Bayesian Kalman algorithm using inverse Wishart distribution to iteratively estimate and correct observation noise $R$ and system noise Q., estimate the covariance of system state and observation noise., and apply it to Kalman filters to improve the adaptive ability of Kalman filter clock bias prediction. Simulation experiments on filtering and clock bias prediction based on precise post ephemeris satellite clock bias data. The results show that compared with traditional Kalman filters., applying variational Bayesian adaptive Kalman filters to clock bias prediction has improved the prediction accuracy to a certain extent.

关键词： Adaptation models Satellites Adaptive systems filtering Noise filtering algorithms Prediction algorithms Bayes methods Kalman filters Clocks

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