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IEEE 17th International Workshop on Multimedia Signal Processing (MMSP 2015)

作者： Shu, Haiyan Jiang, Wenyu Yu, Rongshan ASTAR Inst Infocomm Res Singapore 138632 Singapore

ISBN: (纸本)9781467374781

k-nearest neighbors (kNN) algorithm is a method to find the closest points in a dataset to a query point. The result of kNN can be used for classification and regression, both of which are commonly used in data mining and machine learning. In this paper, Enhanced Vote Count (EVC) circuit, which uses hardware to compare the quantized projected values of query and training/reference vectors instead of the vectors themselves, is considered to approximate the kNN search to provide a low complexity search solution. To improve the performance of EVC with limited projection number because projection number is directly related to implementation cost of EVC circuit, the concept of weak bit is considered and only reliable binary pattern matching is evaluated. The implementation of weak bit based on EVC circuit is also described. Simulation results show that, the performance of EVC can be significantly improved with weak bit implementation under limited projection number.

关键词： k-nearest neighbors algorithm Enhanced Vote Count weak bit

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Surface roughness prediction in micro-plasma transferred arc metal additive manufacturing process using k-nearest neighbors algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2022年第5-6期119卷 2985-2997页

作者： kumar, Pravin Jain, Neelesh kumar Indian Inst Technol Indore Dept Mech Engn Simrol 453552 MP India

Micro-plasma transfer arc metal additive manufacturing (mu-PTAMAM) process is a unique direct energy deposition (DED) type additive manufacturing (AM) process capable of manufacturing three-dimensional metallic components through simultaneous use of wire and powder forms of AM material. DED process yields an uneven surface on the manufactured components which necessitates their further post-processing to attain the required surface finish, dimensional accuracy, and geometrical accuracy. This research focuses on using machine learning algorithm, namely, k-nearest neighbors (kNN), to predict the surface roughness. The data of surface roughness for training kNN algorithm was generated by depositing multi-layer single-track depositions yielding wall-like structures using Stellite-6 as AM material in powder and wire forms. It was found that surface roughness increases with an increase in power supply to micro-plasma and AM material feed rate whereas it decreases with an increase in traverse speed of the deposition head for both powder and wire of the AM material. Surface roughness of the walls for powder form of the AM material is smaller (i.e., 118 to 149 mu m) than that obtained by the wire form (i.e., 195 to 227 mu m). Surface roughness prediction error for kNN algorithm is found to be from - 6.2 to 2.8% for powder form and - 5.8 to 2.3% for wire form of the AM material proving capability of kNN algorithm for accurate prediction of surface roughness produced by mu-PTAMAM process. Since the prediction error depends on number of data set used to train kNN algorithm, therefore, it can be further reduced by increasing number of training data set.

关键词： Directed energy deposition Micro-plasma Surface roughness k-nearest neighbors algorithm Machine learning

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Using the k-nearest neighbors algorithm for Automated Detection of Myocardial Infarction by Electrocardiogram Data Entries

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2019年第4期29卷 730-737页

作者： Savostin, A. A. Ritter, D., V Savostina, G., V Kozybayev North Kazakhstan State Univ Petropavlovsk 150000 Kazakhstan

This article presents a new approach to solving the problem of automated detection of myocardial infarction of various localization by electrocardiogram data entries. Only the second standard lead is used in the analysis. The signal in this lead undergoes digital filtering in order to remove low-frequency and high-frequency interference. Then, individual cardio complexes P-QRS-T are extracted from the signal, and the following parameters are calculated for them: minimum value, maximum value, interquartile range, mean absolute deviation, root mean square, mode, and entropy. Using the calculated parameters, a standardized training (learning) dataset is formed. The classifier model represents the k-nearest neighbors algorithm with the Manhattan metric of the distance between the objects and number of neighbors k = 9. After learning, the classifier shows the results by precision pre = 98.60%, by recall rec = 97.34%, by specificity spec = 95.93%, and by accuracy acc = 97.03%. According to the analysis of the obtained results, the suggested classifier model offers certain advantages as compared to existing alternatives.

关键词： electrocardiogram myocardial infarction classification k-nearest neighbors algorithm

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Study on Weak Bit in Vote Count and its Application in k-nearest neighbors algorithm 10

Study on Weak Bit in Vote Count and its Application in <i>k<...

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10th IEEE Conference on Industrial Electronics and Applications

作者： Shu, Haiyan Jiang, Wenyu Yu, Rongshan ASTAR Inst Infocomm Res Singapore 138632 Singapore

ISBN: (纸本)9781479983896

In the Vote Count for k-nearest neighbors (kNN) algorithm, the quantized projection values of query and training/reference vectors are compared and counted. In this process, not all quantized projection results are reliable for bit-matching and the search result may be distorted by these unreliable bit-matching. In this paper, the concept of weak bit is introduced to identify those unreliable bits after quantization and the corresponding bit-matching comparison is not executed. Simulation results show that, when weak bit is employed, the accuracy of kNN based on Vote Count can be improved significantly.

关键词： Weak bit Vote Count k-nearest neighbors algorithm

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A New Approach in Detectability of Microcalcifications in the Placenta during Pregnancy Using Textural Features and k-nearest neighbors algorithm

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JOURNAL OF IMAGING 2022年第3期8卷 81-81页

作者： Miron, Mihaela Moldovanu, Simona Stefanescu, Bogdan Ioan Culea, Mihai Pavel, Sorin Marius Culea-Florescu, Anisia Luiza Dunarea de Jos Univ Galati Fac Automat Comp Elect Engn & Elect Dept Comp Sci & Informat Technol 47 Domneasca Str Galati 800008 Romania Dunarea de Jos Univ Galati Fac Med & Pharm Dept Clin Surg 47 Domneasca Str Galati 800008 Romania Clin Emergency Hosp Sf Ap Andrei Galati Dept Obstet & Gynecol Galati 800578 Romania Dunarea de Jos Univ Galati Fac Automat Comp Elect Engn & Elect Dept Elect & Telecommun 47 Domneasca Str Galati 800008 Romania

(1) Background: Ultrasonography is the main method used during pregnancy to assess the fetal growth, amniotic fluid, umbilical cord and placenta. The placenta's structure suffers dynamic modifications throughout the whole pregnancy and many of these changes, in which placental microcalcifications are by far the most prominent, are related to the process of aging and maturation and have no effect on fetal wellbeing. However, when placental microcalcifications are noticed earlier during pregnancy, they could suggest a major placental dysfunction with serious consequences for the fetus and mother. For better detectability of microcalcifications, we propose a new approach based on improving the clarity of details and the analysis of the placental structure using first and second order statistics, and fractal dimension. (2) Methods: The methodology is based on four stages: (i) cropping the region of interest and preprocessing steps;(ii) feature extraction, first order-standard deviation (SD), skewness (Sk) and kurtosis (kR)-and second order-contrast (C), homogeneity (H), correlation (CR), energy (E) and entropy (EN)-are computed from a gray level co-occurrence matrix (GLCM) and fractal dimension (FD);(iii) statistical analysis (t-test);(iv) classification with the k-nearest neighbors algorithm (k-NN algorithm) and performance comparison with results from the support vector machine algorithm (SVM algorithm). (3) Results: Experimental results obtained from real clinical data show an improvement in the detectability and visibility of placental microcalcifications.

关键词： placenta calcifications preterm placental calcifications first order feature second order feature t-test k-nearest neighbors algorithm support vector machine algorithm

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Quantum algorithm for k-nearest neighbors Classification Based on the Metric of Hamming Distance

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INTERNATIONAL JOURNAL OF THEORETICAL PHYSICS 2017年第11期56卷 3496-3507页

作者： Ruan, Yue Xue, Xiling Liu, Heng Tan, Jianing Li, Xi Anhui Univ Technol Sch Comp Sci & Technol Maanshan 243005 Peoples R China Southeast Univ Minist Educ Key Lab Comp Network & Informat Integrat Nanjing 210096 Jiangsu Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Jiangsu Peoples R China

k-nearest neighbors (kNN) algorithm is a common algorithm used for classification, and also a sub-routine in various complicated machine learning tasks. In this paper, we presented a quantum algorithm (QkNN) for implementing this algorithm based on the metric of Hamming distance. We put forward a quantum circuit for computing Hamming distance between testing sample and each feature vector in the training set. Taking advantage of this method, we realized a good analog for classical kNN algorithm by setting a distance threshold value t to select k - n e a r e s t neighbors. As a result, QkNN achieves O(n (3)) performance which is only relevant to the dimension of feature vectors and high classification accuracy, outperforms Llyod's algorithm (Lloyd et al. 2013) and Wiebe's algorithm (Wiebe et al. 2014).

关键词： Quantum algorithm k-nearest neighbors algorithm Hamming distance Classification Machine learning

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Monitoring of high-dimensional and high-frequency data streams: A nonparametric approach

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QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT 2025年第3期22卷 506-525页

作者： Wang, Zhiqiong Li, Xin Wang, Ying Ma, Yanhui Xue, Li Tianjin Univ Technol Sch Management Tianjin 300384 Peoples R China Zhengzhou Univ Aeronaut Sch Management Engn Zhengzhou Peoples R China

High-dimensional and high-frequency data streams (HHDS) have become widely available with the prevalence of data-acquisition technologies. Because HHDS have the characteristics of high dimensionality and temporal correlation, traditional statistical process control (SPC) techniques cannot be used directly to address the problem of monitoring HHDS. Most extant monitoring methods commonly assume that the process observations are independent or can be described by some parametric models and face the challenge posed by high-dimensional data. Little research has been conducted on the robust monitoring of HHDS that can simultaneously accommodate high dimensions and high frequencies. Therefore, in this paper, we propose a novel nonparametric approach for monitoring HHDS, based on data decorrelation, dimension reduction, and SPC. Specifically, process observations are first sequentially decorrelated and standardized using Cholesky decomposition, and then the k-nearest neighbors classification algorithm is applied to transform uncorrelated high-dimensional data into one-dimensional data. Finally, the traditional cumulative-sum procedure is used for online monitoring, based on an empirical log-likelihood ratio test. Numerical studies have shown that our monitoring scheme is sufficiently reliable and efficient for the online monitoring of HHDS. An illustrative example about the Dow Jones 30 industrial stock prices is presented to further validate the proposed approach.

关键词： Data decorrelation high-dimensional and high-frequency data streams k-nearest neighbors algorithm nonparametric control charts statistical process control

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Cooperative Spectrum Sensing based on Anomaly Detection and k nearest neighbors

Cooperative Spectrum Sensing based on Anomaly Detection and ...

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IEEE Future Networks World Forum (FNWF)

作者： Lopez-Lopez, Lizeth Andrade, Angel G. Galaviz, Guillermo UABC Fac Ingn Mexicali Baja California Mexico

ISBN: (纸本)9781665462501

Spectrum sensing (SS) is an enabling task for efficient spectrum utilization, which is required by future 6G mobile networks. SS allows cognitive users to dynamically identify the status (present or absent) of primary users (PU) to access the underutilized frequency bands. The use of artificial intelligence algorithms has been proposed to solve the SS problem by treating it as a classification problem. However, this implies the need for vast information for training, which might not be available for all types of PU signals. This paper addresses SS as an anomaly detection (AD) problem, where the "normal" behavior is defined as the absence of the PU. Thus, the presence of the PU is considered an anomaly. The k-nearest neighbors algorithm is implemented after a pre-processing signal stage based on a Decomposition and Recombination algorithm to improve the performance in a cooperative SS (CSS) scenario. Results exhibit the gain in detection performance compared to the conventional energy detector.

关键词： anomaly detection spectrum sensing cognitive radio k-nearest neighbors algorithm decomposition and recombination algorithm

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Multi-GPU 3D k-nearest neighbors computation with application to ICP, point cloud smoothing and normals computation

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PARALLEL COMPUTING 2024年 121卷

作者： Agathos, Alexander Azariadis, Philip Univ Aegean Konstantinoupoleos 1 Syros 84100 Greece Univ West Attica P Ralli & Thivon 250 Athens 12244 Greece

The k-nearest neighbors algorithm is a fundamental algorithm that finds applications in many fields like Machine Learning, Computer Graphics, Computer Vision, and others. The algorithm determines the closest points (d-dimensional) of a reference set R according to a query set of points Q under a specific metric (Euclidean, Mahalanobis, Manhattan, etc.). This work focuses on the utilization of multiple Graphical Processing Units for the acceleration of the k-nearest neighbors algorithm with large or very large sets of 3D points. With the proposed approach the space of the reference set is divided into a 3D grid which is used to facilitate the search for the nearest neighbors. The search in the grid is performed in a multiresolution manner starting from a high-resolution grid and ending up in a coarse one, thus accounting for point clouds that may have non-uniform sampling and/or outliers. Three important algorithms in reverse engineering are revisited and new multi-GPU versions are proposed based on the introduced kNN algorithm. More specifically, the new multi-GPU approach is applied to the Iterative Closest Point algorithm, to the point cloud smoothing, and to the point cloud normal vectors computation and orientation problem. A series of tests and experiments have been conducted and discussed in the paper showing the merits of the proposed multi-GPU approach.

关键词： k-nearest neighbors algorithm Parallel processing Graphical processing units Iterative Closest Point algorithm Point cloud smoothing Point cloud normal vectors computation

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Breast cancer detection using k-nearest neighbors data mining method obtained from the bow-tie antenna dataset

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INTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING 2017年第6期27卷 n/a-N.PAG页

作者： Aydin, Emine Avsar keles, Mumine kaya Adana Sci & Technol Univ Dept Aerosp Engn TR-01180 Adana Turkey Adana Sci & Technol Univ Dept Comp Engn TR-01180 Adana Turkey

Breast cancer, has been a significant cancer type for women on the society. Early diagnosis and timely medical treatment are important key factors spreading to the other tissues and permitting long-time survival of patients. Since the existing methods have several serious shortfalls, microwave imaging method for the diagnosis of early stage tumors has been interested by different scientific research groups in terms of moderating endogenous the electrical property difference between healthy tissue and malignancies. In this article, both an ultra-wideband bow-tie antenna with enhanced bandwidth and a 3D breast model which has different electrical properties which are permittivity and conductivity is created in simulation tool to solve electromagnetic field values. Return loss, VSWR, and radiation pattern characteristics, which are significant antenna parameters, are simulated and obtained whether the antenna possess an efficient characteristic or not. Electric field values over the breast tissue in which there is a tumor or not tumor are evaluated. In this article, above-mentioned values of frequency bandwidth, dielectric constant of antenna's substrate, electric field, and tumor information were consisted in their dataset. This dataset obtained from the Bow-Tie Antenna was used to detect the breast cancer with one of the data mining method, which is k-nearest Neighbor algorithm.

关键词： bow-tie antenna breast cancer detection data mining dielectric properties k-nearest neighbors algorithm microwave imaging

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