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32nd International Telecommunication Networks and Applications Conference (ITNAC)

作者： Ebaid, Emad Navaie, Keivan Univ Lancaster Sch Comp & Commun Lancaster England

ISBN: (纸本)9781665471039

Wi-Fi fingerprinting techniques are commonly used in Indoor Positioning Systems (IPS) as Wi-Fi signal is available in most indoor settings. In such systems, the position is estimated based on a matching algorithm between the enquiry points and the recorded fingerprint data. In this paper, our objective is to investigate and provide quantitative insight into the performance of various Nearest Neighbour (NN) algorithms. The NN algorithms such as knn are also often employed in IPS. We extensively study the performance of several NN algorithms on a publicly available dataset, UJIIndoorLoc. Furthermore, we propose an improved version of the Weighted knn algorithm. The proposed model outperforms the existing works on the UJIIndoorLoc dataset and achieves better results for the success rate and the mean positioning error.

关键词： Indoor positioning Wi-Fi fingerprinting knn algorithm Wknn algorithm data-driven knn

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Research on the application of situation form judgment in command training based on K nearest neighbor data mining algorithm 2

Research on the application of situation form judgment in co...

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2nd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC)

作者： Guo, Weilan Zhang, Yujie Wang, Xin Zhang, Jieqiang Wangchao, Luomeng Lu, Fengyun China Peoples Police Univ Fac Grad Studies Langfang 065000 Hebei Peoples R China China Peoples Police Univ Coll Police Command Langfang 065000 Hebei Peoples R China

ISBN: (纸本)9781510655201;9781510655195

Based on k nearest neighbor algorithm, this paper discusses the training method of assistant commander to judge the situation in command training, and focuses on judging the situation level of a certain area in a specific event. Firstly, knn model is introduced. Then, after understanding the principle of the algorithm, python tool is used to calculate, classify and visualize the test samples of 13 regions. Finally, according to the distance between the test sample and the test sample, the level ' risk ' of the test area X is calculated, and then a reference training method is proposed for the training of decision-making ability in command training to assist command training.

关键词： knn algorithm command training training methods decision making

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Big data outlier detection model based on improved density peak algorithm

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第4期40卷 6185-6194页

作者： Shao, Mengliang Qi, Deyu Xue, Huili Guangzhou Univ South China Inst Software Engn Dept Comp Sci Guangzhou Peoples R China South China Univ Technol Res Inst Comp Syst Guangzhou Guangdong Peoples R China Guangzhou Nanyang Polytech Coll Sch Informat Engn Guangzhou Peoples R China

Outlier detection is an important branch of data mining. This paper proposes an advanced fast density peak outlier detection algorithm based on the characteristics of big data. The algorithm is an outlier detection method based on the improved density peak clustering algorithm. This paper improves the original algorithm. From the perspective of outlier detection, although it is a clustering idea, it avoids the clustering process, reduces the time complexity of the cluster-based outlier detection algorithm, and absorbs. The outlier detection based on neighbors is not sensitive to data dimensions and other advantages. In the power industry, outlier detection can be used in areas such as grid fault detection, equipment fault detection, and power abnormality detection. The simulation experiment of outlier detection based on the daily load curve of single and multiple transformers in a certain province shows that the improved algorithm can effectively detect outliers in the data.

关键词： Outlier detection big data knn algorithm density clustering

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A dynamic anomaly detection method of building energy consumption based on data mining technology

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ENERGY 2023年第PartA期263卷

作者： Lei, Lei Wu, Bing Fang, Xin Chen, Li Wu, Hao Liu, Wei Zhejiang Sci Tech Univ Sch Civil Engn & Architecture Hangzhou 310018 Peoples R China Guangxi Vocat & Tech Coll Commun Coll Civil Engn & Architecture 1258 Kunlun Ave Nanning 530216 Peoples R China Alibaba Grp Alibaba Cloud 969 West Wen Yi Rd Hangzhou 311121 Peoples R China KTH Royal Inst Technol Dept Civil & Architectural Engn Div Sustainable Bldg Brinellvagen 23 S-10044 Stockholm Sweden

Due to the equipment failure and inappropriate operation strategy, it is often difficult to achieve energy-efficient building. Anomaly detection of building energy consumption is one of the important approaches to improve building energy-saving. The great amounts of energy consumption data collected by building energy monitoring platforms (BEMS) provides potentials in using data mining technology for anomaly detection. This study pro-poses a dynamic anomaly detection algorithm for building energy consumption data, which realizes the dynamic detection of point anomalies and collective anomalies. The algorithm integrates unsupervised clustering algo-rithm with supervised algorithm to establish a semi-supervised matching mechanism, which avoids the influence of error label and improves the efficiency of anomaly detection. A particle swarm optimization (PSO) is used to optimize the unsupervised clustering algorithm. This investigation tests the effectiveness of the proposed algo-rithm and evaluates the performance of the energy consumption clustering algorithm by using the annual electricity consumption data of an experimental building in a university. The results show that the clustering accuracy of the algorithm can reach more than 80%, and it can effectively detect the building energy con-sumption data of two different forms of outliers. It can provide reliable data support for adjusting building management strategies.

关键词： Building energy consumption Dynamic anomaly detection Semi -supervised algorithm Particle swarm optimization K-medoids algorithm knn algorithm

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Human motion intention recognition based on EMG signal and angle signal

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COGNITIVE COMPUTATION AND SYSTEMS 2021年第1期3卷 37-47页

作者： Sun, Baixin Cheng, Guang Dai, Quanmin Chen, Tianlin Liu, Weifeng Xu, Xiaorong Beijing Union Univ Beijing Key Lab Informat Serv Engn Beijing Peoples R China Beijing Union Univ Sch Urban Rail Transit & Logist Beijing Peoples R China

As the traditional single biological signal or physical signal is not good at predicting the angle value of the knee joint, the innovative fusion of biological signals and physical signals is used to analyze the movement posture of the lower limbs. In order to solve the problem of human movement intention recognition, a wearable is designed. The signal-acquisition experiment platform uses muscle electrical signals and joint angle signals as motion data. After the signals are processed, the knn algorithm is used to identify the four gait motion modes of the human body to walk naturally, climb stairs, descend stairs, and cross obstacles. The test results show that it is feasible to use the knn algorithm to analyze the strength of the active and passive muscles of the knee joint movement according to different thigh lift heights, and to predict the knee joint angle value when the human body goes up and down the stairs. The comprehensive prediction accuracy rate reaches 91.45%.

关键词： medical signal processing human movement intention recognition joint angle signals muscle electrical signals knee joint movement human motion intention recognition gait motion modes gait analysis biomechanics electromyography angle signal motion data knee joint angle value physical signal muscle human body biological signals movement posture traditional single biological signal signal-acquisition experiment platform knn algorithm

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Research on Fault Diagnosis of Wind Power Generator Blade Based on SC-SMOTE and knn

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JOURNAL OF INFORMATION PROCESSING SYSTEMS 2020年第4期16卷 870-881页

作者： Peng, Cheng Chen, Qing Zhang, Longxin Wan, Lanjun Yuan, Xinpan Hunan Univ Technol Sch Comp Sci Zhuzhou Peoples R China

Because SCADA monitoring data of wind turbines are large and fast changing, the unbalanced proportion of data in various working conditions makes it difficult to process fault feature data. The existing methods mainly introduce new and non-repeating instances by interpolating adjacent minority samples. In order to overcome the shortcomings of these methods which does not consider boundary conditions in balancing data, an improved over-sampling balancing algorithm SC-SMOTE (safe circle synthetic minority oversampling technology) is proposed to optimize data sets. Then, for the balanced data sets, a fault diagnosis method based on improved k-nearest neighbors (knn) classification for wind turbine blade icing is adopted. Compared with the SMOTE algorithm, the experimental results show that the method is effective in the diagnosis of fan blade icing fault and improves the accuracy of diagnosis.

关键词： Fault Diagnosis knn algorithm SCADA dataset SC-SMOTE algorithm

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Using python libraries and k-Nearest neighbors algorithms to delineate syn-sedimentary faults in sedimentary porous media

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MARINE AND PETROLEUM GEOLOGY 2023年 153卷

作者： Martin-Martin, Manuel Bullejos, Manuel Cabezas, David Alcala, Francisco Javier Univ Alicante Dept Ciencias Tierra & Medio Ambiente AP 99 Alicante 03080 Spain Univ Granada Dept Algebra Granada 18010 Spain Univ Granada Dept Anal Matematico Granada 18010 Spain Dept Desertificac & Geoecol Estn Expt Zonas Aridas Almeria 04120 Spain Univ Autonoma Chile Fac Ingn Inst Ciencias Quim Aplicadas Santiago 7500138 Chile Univ Alicante Dept Ciencias Tierra & Medio Ambiente Campus San VicenteSan Vicente Raspeig Alicante 03080 Spain

This paper introduces a methodology based on Python libraries and machine learning k-Nearest Neighbors (knn) algorithms to create an interactive 3D HTML model (3D_Vertical_Sections_Faults_***) that combines 2D grain-size knn-prediction vertical maps (vertical sections) from which syn-sedimentary faults and other features in sedimentary porous media can be delineated. The model can be visualized and handled with conventional web browsers. The grain-size physical parameter is measurable, constant over instrumental time, handleable math-ematically, and its range can be associated to lithological classes. Grain-size input data comes from a public database of 433 boreholes in the Llobregat River Delta (LRD) in NE Spain. Four lithological classes were defined: Pre-Quaternary basement, and Quaternary gravel, sand, and clay-silt. Using a new knn-prediction algorithm, seven NW-SE (transversal) and three SW-NE (longitudinal) vertical sections were created following the orien-tation of faults identified in surface and detected in reflection seismic geophysical surveys. For exploratory K values in the 1-75 range were used. K around 25 provides the general and smoothy shape of the basement top surface, whereas K =1 is a optimal value to represent the heterogeneity of the LRD at short distance. Using a new knn-prediction confidence algorithm inspired in the Similarity Ratio algorithm for machine-learning knn, the vertical sections overall confidence was evaluated as satisfactory. A general decreasing confidence trend ac-cording to the decreasing data density with depth and from inland to seaward was found. The vertical sections created with K = 1 show horizontal interruptions (displacements or vertical steps) in the basement continuity and in the Quaternary coarse bodies (gravel and sand) attributable to the action of Quaternary active faults. These faults have been linked or correlated with well-known active faults in the area related in much cases with the Valencia Trough

关键词： Python libraries knn algorithm Syn-sedimentary faults 3D stratigraphic architecture Llobregat river delta NE Spain

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Smart Machine Health Prediction Based on Machine Learning in Industry Environment

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INFORMATION 2023年第3期14卷 181页

作者： Yeruva, Sagar Gunuganti, Jeshmitha Kalva, Sravani Salkuti, Surender Reddy Kim, Seong-Cheol VNR Vignana Jyothi Inst Engn & Technol Dept CSE AIML & IoT Hyderabad 500090 India VNR Vignana Jyothi Inst Engn & Technol Dept CSE Hyderabad 500090 India Woosong Univ Dept Railroad & Elect Engn Daejeon 34606 South Korea

In an industrial setting, consistent production and machine maintenance might help any company become successful. Machine health checking is a method of observing the status of a machine to predict mechanical mileage and predict the machine's disappointment. The most often utilized traditional approaches are reactive and preventive maintenance. These approaches are unreliable and wasteful in terms of time and resource utilization. The use of system health management in conjunction with a predictive maintenance strategy allows for the scheduling of maintenance times in such a way that device malfunction is avoided, and thus the repercussions are avoided. IoT can help monitor equipment health and provide the best outcomes, especially in an industrial setting. Internet of Things (IoT) and machine learning models are quite successful in providing ongoing knowledge and comprehensive study on infrastructure performance. Our suggested technique uses a mobile application that seeks to anticipate the machine's health status using a classification method utilizing IoT and machine learning technologies, which might benefit the industry environment by alerting the appropriate maintenance team before inflicting significant harm to the system and disrupting normal operations. A comparison of decision tree, XGBoost, SVM, and knn performance has been carried out. According to our findings, XGBoost achieves higher classification accuracy compared to the other algorithms. As a result, this model is selected for creating a user-based application that allows the user to easily check the state of the machine's health.

关键词： Decision Tree algorithm IoT knn algorithm machine health prediction predictive maintenance SVM algorithm XGBoost algorithm

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A Novel Forecastive Anomaly Based Botnet Revelation Framework for Competing Concerns in Internet of Things

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JOURNAL OF APPLIED SECURITY RESEARCH 2021年第2期16卷 258-278页

作者： Bhatt, Priyang Thakker, Bhaskar Gujarat Technol Univ Comp Engn Ahmadabad 382424 Gujarat India Symbiosis Int Deemed Univ SIDU Symbiosis Inst Technol SIT Pune Maharashtra India

With internet, billions and millions of devices in Internet of Things (IoT) are interconnected and are communicated with other devices through messaging bots. The messaging bots are sometimes controlled by the attackers so as to carry out several malicious activities. Thus bots become a serious cyber security hazard for the IoT devices. For this reason, it is crucial to detect the existence of malicious bots and other anomalies in the network. Thus to tackle with these bots and anomalies a Novel Forecastive Anomaly based Botnet Revelation Framework is designed in our proposed work. The approach works as a two way progression, i.e. first is the Instance Creation and the second is Cataloging. As an alternative to machine learning algorithm, in our work, an Ensemble based Stream Mining is being used to generate several instances with less memory and time. Once the instances are created, Graph Structure Based Detection of Anomaly (GSBDA) is initiated based on features derived by the stream mining algorithm to detect the presence of hazardous anomalies. In addition, the second phase utilizes a knn (K Nearest neighbor) algorithm, a type of instance based learning algorithm. It is used to identify the Botnet accurately by observing the network flows.

关键词： IoT anomaly detection botnet knn algorithm ensemble based stream mining instance creation cataloging

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Discrimination of unknown complex network based on the information of local nodes 40

Discrimination of unknown complex network based on the infor...

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40th Chinese Control Conference (CCC)

作者： Liu, Siyuan Jin, Yue Wu, Yongjun Li, Kun Zhang, Jianlei Nankai Univ Coll Artificial Intelligence Dept Automat Tianjin 300071 Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin 300071 Peoples R China Tiangong Univ Sch Math Sci Tianjin 300387 Peoples R China Hebei Univ Technol Sch Civil & Transportat Engn Tianjin 300401 Peoples R China

ISBN: (纸本)9789881563804

At present, the study on complex network and game theory has been spread across the fields of science and engineering. The research contents on these topics mainly focus on the evolution results of structured populations based on the determined network structures and game types, which may not be open information to the interacting players. Therefore, how to obtain the maximum benefit in an unfamiliar game environment for an individual new to the population is a topic that has received little attention but is of great significance. In this paper, we explore how to use the minimum amount of known information available to realize the identification of large-scale unknown networks. We simulate real large structured populations with five classical large complex networks and hide their real structures. Then, we propose an improved k-nearest neighbor (knn) algorithm, which can infer the real network structures with high accuracy only based on the degree and clustering coefficient information of a few local nodes on the network. The results and method presented here provide some hints about the study on the evolutionary game process and swarm intelligence in networked populations.

关键词： Complex network Structured population Game theory knn algorithm Classification

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