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A novel single-ended fault location scheme for parallel transmission lines using k-nearest neighbor algorithm

COMPUTERS & ELECTRICAL ENGINEERING 2018年 69卷 41-53页

作者： Swetapadma, Aleena Yadav, Anamika KIIT Univ Sch Comp Engn Bhubaneswar Odisha India NIT Raipur Dept Elect Engn Chhattisgarh India

This paper proposes k-nearest neighbour (k-NN)-based method for fault location estimation of all types of fault in parallel lines using one-terminal measurement. Discrete Fourier Transform (DFT) is used for pre-processing the signals and then the standard deviation of one cycle of pre-fault and one cycle of post-fault samples are used as inputs to k-NN algorithm. The results obtained under various fault conditions demonstrate the high accuracy of the proposed scheme to estimate the fault location. The accuracy of the k-NN-based fault location scheme is not affected by alteration in fault type including inter-circuit faults, fault location, fault inception angle, fault resistance, and pre-fault load angle.

关键词： Discrete Fourier transform Fault location k-nearest neighbor algorithm Parallel transmission lines

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Uncertainty prediction method for traffic flow based on k-nearest neighbor algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第2期39卷 1489-1499页

作者： Yang, Lingmin Hubei Univ Educ Coll Management Wuhan 430205 Peoples R China

In order to overcome the problem of low fitting between traffic uncertainty prediction results and actual values in existing research methods, a traffic flow uncertainty prediction method based on k-nearest neighbor algorithm is proposed. The original database, classification center database, k-nearest neighbor database and intermediate search database are used to construct the database needed in the prediction process. Based on the database, multivariate linear regression is used to assign weights to state variables, and k-nearest neighbor algorithm and kalman filter are used to update the weights to adapt to the uncertainties of traffic flow until the predicted values are obtained, and the uncertainties of traffic flow are predicted. The experimental results show that the maximum average absolute error and average relative error of the proposed method are 0.018 and 0.02, respectively. Compared with the traditional method, the proposed method has higher overall prediction accuracy, higher fitting degree, and is feasible.

关键词： k-nearest neighbor algorithm traffic flow uncertainty prediction kalman filtering concept

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Prediction of protein phenotype based on protein interaction network by coupling genetic algorithm and k-nearest neighbor algorithm

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ANALYTICAL METHODS 2014年第14期6卷 5281-5289页

作者： Li, Zhanchao Chen, Lili Lai, Yanhua Xie, Yun Dai, Zong Zou, Xiaoyong Guangdong Pharmaceut Univ Sch Chem & Chem Engn Guangzhou 510006 Guangdong Peoples R China Sun Yat Sen Univ Sch Chem & Chem Engn Guangzhou 510275 Guangdong Peoples R China

Quick and accurate identification of protein phenotype is a key step for understanding life at the molecular level, and has a significant impact in the fields of biomedicine and pharmacy. However, as a result of genome and other sequencing projects, there is a huge gap between the number of discovered proteins and the number of phenotype annotated proteins. Therefore, it is indispensable to develop an automated and reliable method for predicting protein phenotype. In this paper, a novel method is proposed and used to identify protein phenotype. It is featured by coupling the genetic algorithm and k-nearest neighbor algorithm, and the feature vector is introduced to take into account the information of the protein and the neighboring proteins in the protein interaction network. As a demonstration, a fivefold cross-validation test and independent test set are performed, and the results indicate that the current method may serve as an important complementary tool for other existing algorithms in this area. The source code of MATLAB is freely available on request from the authors.

关键词： Phenotype k-nearest neighbor algorithm Eigenvector Protein Interaction Maps TEST SETS Protein nearest neighbor algorithm Genetic algorithm (GA)

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Solving vehicle routing problem by using improved k-nearest neighbor algorithm for best solution

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年第Jul.期21卷 232-240页

作者： Mohammed, Mazin Abed Abd Ghani, Mohd khanapi Hamed, Raed Ibraheem Mostafa, Salama A. Ibrahim, Dheyaa Ahmed Jameel, Humam khaled Alallah, Ahmed Hamed Univ Tekn Malaysia Melaka Fac Informat & Commun Technol Biomed Comp & Engn Technol BIOCORE Appl Res Grp Durian Tunggal Malaysia Univ Anbar Univ Headquarter Planning & Follow Up Dept Anbar Iraq Univ Human Dev Coll Sci & Technol Dept IT Sulaymaniyah Krg Iraq Univ Tun Hussein Onn Malaysia Fac Comp Sci & Informat Technol Johor Baharu Malaysia Univ Anbar Coll Comp Sci & Informat Technol Informat Syst Dept Anbar Iraq

Context: Vehicle routing problem (VRP) is one of the many difficult issues that have no perfect solutions yet. Many researchers over the last few decades have established numerous researches and used many methods with different techniques to handle it. But, for all research, finding the lowest cost is very complex. However, they have managed to come up with approximate solutions that differ in efficiencies depending on the search space. Problem: In this study the problem is as follows: have a number of vehicles which are used for transporting applications to instance place. Each vehicle starts from a main location at different times every day. The vehicle picks up applications from start locations to the instance place in many different routes and return back to the start location in at specific times every day, starting from early morning until the end of official working hours, on the following conditions: (1) Every location will be visited once in each route, and (2) The capacity of each vehicle is enough for all applications included in each route. Objectives: Our paper attempt to find an optimal route result for VRP by using k-nearest neighbor algorithm (kNNA). To achieve an optimal solution for VRP with the accompanying targets: (1) To reduce the distance and the time for all paths this leads to speedy the transportation of customers to their locations, (2) To implement the capacitated vehicle routing problem (CVRP) model for optimizing the solutions. Approach: The approach has been presented based on two phases: firstly, the algorithms have been adapted to solve the research problem, where its procedure is different than the common algorithm. The structure of the algorithm is designed so that the program does not require a large database to store the population, which speeds up the implementation of the program execution to obtain the solution;secondly, the algorithm has proven its success in solving the problem and finds a shortest route. For the purpose of

关键词： k-nearest neighbor algorithm Vehicle routing problem Capacitated vehicle routing problem

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dForml(kNN)-PseAAC: Detecting formylation sites from protein sequences using k-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

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JOURNAL OF THEORETICAL BIOLOGY 2019年 470卷 43-49页

作者： Ning, Qiao Ma, Zhiqiang Zhao, Xiaowei Northeast Normal Univ Informat Sci & Technol Changchun 130117 Jilin Peoples R China

Formylation is a type of post-translational modification that can occur on lysine sites, which plays an irreplaceable role in organism. To better understand the mechanism, it is necessary to identify formylation sites in proteins accurately. Computational method is popular because of its more convenience and higher speed than traditional experimental methods. However, no computational method has been proposed for prediction of lysine formylation. In this study, we developed a predictor named LFPred to identify lysine formylation sites using sequence features (including amino acid composition (AAC), binary profile features (BPF), and amino acid index (AAI)) combined k-nearest neighbor algorithm as classifier. We chose discrete window instead of continuous window according to information entropy. Besides, we took measure to select more reliable negative samples and address the severe imbalance between positive samples and negative samples. Finally, the performance of LFPred is measured with a specificity of 79.9% and a sensibility of 81.4% using jackknife test method, which indicated that our method can be a useful tool for prediction of lysine formylation sites. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Formylation Sequence feature k-nearest neighbor algorithm Discrete window Information entropy

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Packer Identification Method for Multi-Layer Executables Using Entropy Analysis with k-nearest neighbor algorithm

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2023年第3期E106A卷 355-357页

作者： Omachi, Ryoto Murakami, Yasuyuki Osaka Electrocommun Univ Neyagawa 5728530 Japan

The damage cost caused by malware has been increasing in the world. Usually, malwares are packed so that it is not detected. It is a hard task even for professional malware analysts to identify the packers especially when the malwares are multi-layer packed. In this letter, we propose a method to identify the packers for multi-layer packed malwares by using k-nearest neighbor algorithm with entropy-analysis for the malwares.

关键词： key malware packer identification multi-layer packing k-nearest neighbor algorithm entropy analysis

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BEARING LIFE PREDICTION MODEL FOR ELECTROMECHANICAL EQUIPMENT BY INTEGRATING DEEP NEURAL NETWORk AND k-nearest neighbor algorithm AND ITS APPLICATION

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JOURNAL OF THEORETICAL AND APPLIED MECHANICS 2024年第4期62卷 721-735页

作者： Ma, Yiming Jilin Jianzhu Univ Changchun Peoples R China

Current life prediction methods of Electromechanical equipment bearings have issues of low accuracy and lack of stability. To address these problems, firstly, indicators based on life degradation characteristics of bearings are selected. Then, a deep neural network-based life prediction model is constructed. Finally, the k-nearest neighbor algorithm is introduced to correct the deviation of the deep neural network prediction model, and a hybrid life prediction model is designed. Results show that effectiveness of the designed model was better, which was of great practical significance for detecting bearing failures in advance, reducing equipment losses and improving equipment reliability.

关键词： bearings deep neural network k-nearest neighbor algorithm deviation life pre- diction model

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Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on k-nearest neighbor algorithm

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Transactions of Tianjin University 2018年第3期24卷 282-289页

作者： Denghua Zhong Rongxiang Du Bo Cui Binping Wu Tao Guan State Key Laboratory of Hydraulic Engineering Simulation and Safety School of Civil EngineeringTianjin University

During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the k-nearest neighbor algorithm. Taking the LHk core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface.

关键词： Core rockfill dam Dam storehouse surface construction Spreading thickness k-nearest neighbor algorithm Real-time monitor

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k-nearest neighbor algorithm for selection of new gelatin-carboxymethylcellulose films with TiO2 nanoparticles and propolis extract with antioxidant and light barrier activity

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FOOD PACkAGING AND SHELF LIFE 2023年 38卷

作者： de Brito, Raira Sa Costa, Marcos Jhony Almeida Barbosa, Jhonatas Rodrigues Filho, Adilson Ferreira Santos Farias, Fabricio de Souza Lourenco, Lucia de Fatima Henriques Fed Univ Para UFPA Inst Technol ITEC Food Sci & Technol Dept Rua Augusto Correa S-N BR-66075900 Belem PA Brazil Fed Univ Para UFPA Fac Informat Syst BR-68400000 Cameta PA Brazil

Nanoparticles associated with antioxidant ingredients emerge as a new strategy to improve food packaging properties. In this study, we developed gelatin-carboxymethylcellulose films containing propolis extract and TiO2 nanoparticles (1 % and 5 %), and the k-nearest neighbor algorithm was used to select the best packaging composition. Films showed excellent antioxidant activity and those mixed with nanoparticles performed better with light and water barrier properties. The film with propolis and 5 % TiO2 nanoparticles showed higher antioxidant activity and opacity, lower water vapor permeability, solubility, water activity, moisture content, and thickness. These results indicate that the kNN algorithm, an artificial intelligence tool, is promising for classifying and selecting films with properties of interest. The films containing propolis extract and TiO2 nanoparticles have excellent properties that validate their application as food packaging.

关键词： k-nearest neighbor algorithm Nanoparticles Propolis extract Food packaging

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An Optimized k-nearest neighbor algorithm for Extending Wireless Sensor Network Lifetime 3rd

An Optimized K-Nearest Neighbor Algorithm for Extending Wire...

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3rd International Conference on Advanced Machine Learning Technologies and Applications (AMLTA)

作者： Ahmed, Mohammed M. Taha, Ayman Hassanien, Aboul Ella Hassanien, Ehab Minia Univ Fac Comp & Informat Al Minya Egypt Cairo Univ Fac Comp & Informat Giza Egypt Sci Res Grp Egypt SRGE Giza Egypt

ISBN: (纸本)9783319746906;9783319746890

This paper presents an optimized k-nearest neighbors (kNNs) classification algorithm using the metaheuristic whale optimization to searches for sink node in wireless sensor networks. Sink node aggregate data from all sensor nodes and reducing the energy consumption network to prolong network lifetime. To reach aforementioned, a fitness function has formulated to choose the best location of sink node with high residual neighbor's sensor nodes energy to leads to maximizing the network lifetime. Eventually, the experimental results have been conducted whereas sensor nodes are propagated in a random location within the desired network area. The system has 11% improvement on the network's energy consumption that increases the lifetime of the network.

关键词： k-nearest neighbor algorithm Energy-efficient Classification Wireless sensor networks Swarm optimization

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