检索结果-内蒙古大学图书馆

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

SN Computer Science 2024年第5期5卷 1-10页

作者： Tan, Xiao Yiu, Siu Ming Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters. © The Author(s) 2024.

关键词： Acoustic features Anomalous sound detection Dimension reduction Guided genetic algorithm Internet of things (IoT) unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

An unsupervised automatic measurement of wheat spike dimensions in dense 3D point clouds for field application

引用

BIOSYSTEMS ENGINEERING 2022年第PartB期223卷 103-114页

作者： Wang, Fuli Li, Fengping Mohan, Vishwanathan Dudley, Richard Gu, Dongbing Bryant, Ruth Univ Essex Sch Comp Sci & Elect Engn Colchester England Natl Phys Lab Hampton Rd Teddington Middx England RAGT Seeds Ltd Grange Rd Ickleton Essex England

An accurate measurement of field-grown wheat traits, including spike number, dimension and volume are essential for crop phenotyping and yield analysis. A high-throughput method to image field-grown wheat in three dimensions is presented with an accompanying unsu- pervised measuring method to obtain individual wheat spike data. Images are captured using four structured light scanners on a field mobile platform, creating dimensionally accurate sub-millimetre resolution 3D point clouds for a 4.5 m3 volume in less than 10 s. The unsu- pervised method analyses each trial plot's 3D point cloud, containing hundreds of wheat spikes, calculating the average size of the wheat spike and total spike volume per plot. The analysis utilises an adaptive k-means algorithm with dynamic perspectives, to fit each spike's shape and measures the dimensions with a random sample consensus algorithm. The method generates small cuboids to fit all the wheat spikes and estimate the total spikes volume. Experimental results show that the proposed algorithm is a reliable tool for identi- fying spikes from wheat crops and identifying individual spikes. Compared with the manual measurement, according to the results of five scenes, the average error rate in the number of spikes, spikes' height and spikes' width in tests were 16.27%, 5.24% and 12.38% respectively.(c) 2021 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： k-means Point clouds Shape-fitting unsupervised algorithm Wheat phenotype

来源：评论

学校读者我要写书评

暂无评论

Automatic EPS Calculation Guided Genetic algorithm and Incremental PCA Based DBSCAN of Extracted Acoustic Features for Anomalous Sound Detection 3rd

Automatic EPS Calculation Guided Genetic Algorithm and Incre...

引用

3rd International Conference on Advanced Research in Technologies, Information, Innovation and Sustainability, ARTIIS 2023

作者： Tan, Xiao Yiu, Siu Ming Department of Computer Science University of Hong Kong Pokfulam Road Hong Kong

ISBN: (纸本)9783031489297

This paper focuses on automatic detection of anomalies in audio files. We introduce an automated unsupervised algorithm which integrates AEC-guided Genetic algorithm and Incremental PCA and DBSCAN to detect the anomaly sound after extracting acoustic features of the normal environments. Technically, Automatic EPS Calculation algorithm (AEC) based Genetic algorithm optimizes the automatic clustering algorithm’s configuration for Incremental Principal Components Analysis (IPCA) and Density Based Spatial Clustering algorithm with Noise (DBSCAN) to reduce the number of effective components, which is calculated by guided GA, to a manageable count. The DBSCAN uses the output of the PCA and is able to generate predictions with relatively high accuracy. Besides, because of the challenges when selecting the optimized configuration of the IPCA and DBSCAN especially in complex sounds’ scenarios, this paper also introduces a self-adaptative architecture trying to select an optimized set of parameters based on different test environments. Experiments show that our results are better than the existing methods with less computing costs and more stability. The algorithm is generic and can be applied to detect anomalies in machines so as to give an early warning to maintenance people to avoid serious accidents or disasters. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Acoustic Features Automatic Anomalous Sound Detection (ASD) DBSCAN Enhanced Incremental PCA Machine learning SKL Spectrum-Temporal Factors Analysis SVD unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

Using Cluster Correspondence Analysis to Explore Rainy Weather Crashes in Louisiana

引用

TRANSPORTATION RESEARCH RECORD 2022年第8期2676卷 159-173页

作者： Rahman, M. Ashifur Das, Subasish Sun, Xiaoduan Univ Louisiana Dept Civil Engn Lafayette LA 70504 USA Texas A&M Univ Texas A&M Transportat Inst San Antonio TX USA

Rainy weather significantly affects traffic safety, especially in the State of Louisiana. With the aim of identifying the patterns of collective association of attributes in rainfall-involved crashes statewide, crashes that occurred during rainy weather and resulted in two injury groups, fatal and severe injury (FSI) and moderate injury (MI), were extracted from databases acquired from the Louisiana Department of Transportation and Development. A total of 3,381 crashes were extracted, comprising 502 FSI crashes (14.85%) and 2,879 MI crashes (85.15%). This study applied the cluster correspondence analysis (CCA) method, a unique method in combination with cluster analysis and correspondence analysis, to generate clusters by partitioning of individual attributes based on the profiles over the categorical variables identified through dimensional reduction of the dataset. In addition to the biplots illustrating the association of all attributes in the clusters, the top 20 standardized residuals indicating the stronger association are presented in bar plots. Four optimum clusters from FSI and MI crashes reveal that the association of roadway, crash environment, and driver condition characteristics identified in the clusters are highly distinguishable across roadway functional classes. Specifically, varieties of attributes linked to speed limit, lighting condition, alignment, area type, manner of collision, restraint usage, and alcohol/drug can have associative impacts on these two injury severities. The identified associations of crash attributes across various functional class roadways could provide valuable understanding for the development of countermeasures which prioritize the prevention of fatalities and injuries.

关键词： cluster correspondence analysis unsupervised algorithm rainy weather crashes road weather weather related crashes

来源：评论

学校读者我要写书评

暂无评论

W3Detector: Detecting Fraudulent Online Sellers Based on Temporal and Spacial Information 22

W3Detector: Detecting Fraudulent Online Sellers Based on Tem...

引用

22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Tong, Shengbo Xie, Yuyang Liu, Shenghua Yu, Wenjian Cai, Jixuan BNRist Tsinghua University Dept. Computer Science & Tech. Beijing China Insititute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Tencent Inc. ShenZhen China

ISBN: (纸本)9798350345346

With the rapid development of electronic payment, risks in large-scale financial transaction networks are persistently arising. One important problem is how to use the transaction data to detect sellers who do fraudulent trades on the online platform. This paper analyzes three attributes of the transaction flow: money (what), time (when), region (where), and summarizes corresponding characteristics among fraudulent sellers. Based on this, we propose an unsupervised anomaly detection algorithm: W3Detector. It processes the raw data with a new sequence discretization method and uses statistical tools to convert data indicators into information value. With the minimum description length principle, we finally filter out the suspicious sellers. The algorithm is not affected by factors such as unit or range of the attributes, and is highly flexible and scalable. After running on three real-world transaction datasets collected from WeChat Pay, W3Detector has show significantly better performance than the baseline methods of anomaly detection, with weighted accuraccy (WACC) increased by 1.13X and F-score increased by 0.105 on average, and the recall value over 60%. © 2023 IEEE.

关键词： anomaly detection e-commerce business fraudulent trade unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

Clinical and Functional Outcomes in Faller and Non-Faller Older Adults Clustered by Self-Organizing Maps: A Machine-Learning Approach

引用

APPLIED SCIENCES-BASEL 2024年第16期14卷 7093页

作者： Almeida, Milena L. S. Cavalcanti, Aline O. Sarai, Rebeca Silva, Mateus A. Melo, Paulo R. V. Silva, Amanda A. M. Caldas, Rafael R. Buarque, Fernando Trombini-Souza, Francis Univ Pernambuco Dept Phys Therapy BR-56328900 Petrolina PE Brazil Univ Pernambuco Masters & Doctoral Programs Rehabil & Funct Perfor BR-56328900 Petrolina PE Brazil Univ Pernambuco Polytech Sch Engn Recife PE Brazil

Featured Application Increased cognitive demands during motor tasks emerged as a critical discriminator of functional patterns among older adults, shedding light on the intricate interplay between cognitive function and motor performance in fall prevention strategies in community-dwelling older *** A wide range of outcomes makes identifying clinical and functional features distinguishing older persons who fall from non-fallers challenging, especially for professionals with less clinical experience. Thus, this study aimed to map a high-dimensional and complex clinical and functional dataset and determine which outcomes better discriminate older adults with and without self-reported falls. For this, clinical, functional, and cognitive outcomes of 60 community-dwelling older adults classified as fallers and non-fallers were selected based on self-report of a single fall in the last 12 months. An unsupervised intelligent algorithm (Self-Organizing Maps-SOM) was used to cluster and topographically represent the data studied. The SOM model mapped and identified two different groups (topographic error: 0.00;sensitivity: 0.77;precision: 0.42;accuracy: 0.53;F1-score: 0.55) based on self-report of a single fall. We concluded that although two distinct groups were mapped and clustered by the SOM, participants were not necessarily fallers or non-fallers. The increased cost of cognitive demands regarding a motor task (Timed Up and Go Test) and the effect of the Trail Making Test (TMT) Part B regarding TMT Part A could discriminate the functional and cognitive patterns in community-dwelling older adults. Therefore, in clinical practice, identifying patterns involving the interaction between cognition and motor skills, even in once-only faller older adults, can be an efficient approach to assessment and, consequently, to compound intervention programs to prevent falls in this population.

关键词： falls artificial intelligence functional mobility unsupervised algorithm older adults

来源：评论

学校读者我要写书评

暂无评论

Local outlier factor for anomaly detection in HPCC systems

引用

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2024年 192卷

作者： Adesh, Arya Shobha, G. Shetty, Jyoti Xu, Lili RV Coll Engn Bengaluru India RV Univ Bengaluru India HPCC Syst LexisNexis Risk Solut Alpharetta GA USA

Local Outlier Factor (LOF) is an unsupervised anomaly detection algorithm that finds anomalies by assessing the local density of a data point relative to its neighborhood. Anomaly detection is the process of finding anomalies in datasets. Anomalies in real-time datasets may indicate critical events like bank frauds, data compromise, network threats, etc. This paper deals with the implementation of the LOF algorithm in the HPCC Systems platform, which is an open-source distributed computing platform for big data analytics. Improved LOF is also proposed which efficiently detects anomalies in datasets rich in duplicates. The impact of varying hyperparameters on the performance of LOF is examined in HPCC Systems. This paper examines the performance of LOF with other algorithms like COF, LoOP, and kNN over several datasets in the HPCC Systems. Additionally, the efficacy of LOF is evaluated across big-data frameworks such as Spark, Hadoop, and HPCC Systems, by comparing their runtime performances.

关键词： Anomaly detection Local outlier factor algorithm Distributed systems HPCC Systems ECL unsupervised algorithm Big data

来源：评论

学校读者我要写书评

暂无评论

Automated Crop Residue Estimation via unsupervised Techniques Using High-Resolution UAS RGB Imagery

引用

REMOTE SENSING 2024年第7期16卷 1135页

作者： Azimi, Fatemeh Jung, Jinha Purdue Univ Lyles Sch Civil Engn W Lafayette IN 47907 USA

Crop Residue Cover (CRC) is crucial for enhancing soil quality and mitigating erosion in agricultural fields. Accurately estimating CRC in near real-time presents challenges due to the limitations of traditional and remote sensing methods. This study addresses the challenge of accurately estimating CRC using unsupervised algorithms on high-resolution Unmanned Aerial System (UAS) imagery. We employ two methods to perform CRC estimation: (1) K-means unsupervised algorithm and (2) Principal Component Analysis (PCA) along with the Otsu thresholding technique. The advantages of these methods lie in their independence from human intervention for any supervised training stage. Additionally, these methods are rapid and suitable for near real-time estimation of CRC as a decision-making support in agricultural management. Our analysis reveals that the K-means method, with an R2=0.79, achieves superior accuracy in CRC estimation over the PCA-Otsu method with an R2=0.46. The accuracy of CRC estimation for both corn and soybean crops is significantly higher in winter than in spring, attributable to the more weathered state of crop residue. Furthermore, CRC estimations in corn fields exhibit a stronger correlation, likely due to the larger size of corn residue which enhances detectability in images. Nevertheless, the variance in CRC estimation accuracy between corn and soybean fields is minimal. Furthermore, CRC estimation achieves the highest correlation in no-till fields, while the lowest correlation is observed in conventionally tilled fields, a difference likely due to the soil disturbance during plowing in conventional tillage practices.

关键词： crop residue cover tillage practice agricultural management practices line-point transect UAS imagery unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

A Novel Single-Sample Retinal Vessel Segmentation Method Based on Grey Relational Analysis

引用

SENSORS 2024年第13期24卷 4326页

作者： Wang, Yating Li, Hongjun Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

Accurate segmentation of retinal vessels is of great significance for computer-aided diagnosis and treatment of many diseases. Due to the limited number of retinal vessel samples and the scarcity of labeled samples, and since grey theory excels in handling problems of "few data, poor information", this paper proposes a novel grey relational-based method for retinal vessel segmentation. Firstly, a noise-adaptive discrimination filtering algorithm based on grey relational analysis (NADF-GRA) is designed to enhance the image. Secondly, a threshold segmentation model based on grey relational analysis (TS-GRA) is designed to segment the enhanced vessel image. Finally, a post-processing stage involving hole filling and removal of isolated pixels is applied to obtain the final segmentation output. The performance of the proposed method is evaluated using multiple different measurement metrics on publicly available digital retinal DRIVE, STARE and HRF datasets. Experimental analysis showed that the average accuracy and specificity on the DRIVE dataset were 96.03% and 98.51%. The mean accuracy and specificity on the STARE dataset were 95.46% and 97.85%. Precision, F1-score, and Jaccard index on the HRF dataset all demonstrated high-performance levels. The method proposed in this paper is superior to the current mainstream methods.

关键词： retinal vessel segmentation grey relational analysis adaptive discriminative filtering unsupervised algorithm

来源：评论

学校读者我要写书评

暂无评论

Experimental Evaluation of Insider Threat Detection Methods Based on Temporal Representation 10

Experimental Evaluation of Insider Threat Detection Methods ...

引用

IEEE 10th International Conference on Information, Communication and Networks (ICICN)

作者： Lu, Gaole Zhang, Huixiang Liu, Tianluo Liao, Kaihua Feng, Chun Northwestern Polytech Univ Sch Cyberspace Secur Xian Peoples R China

ISBN: (纸本)9781665490825

At present, insider threat detection faces many challenges, such as imbalanced data sets, the complexity of malicious behavior, high data dimension, and difficulty for manual analysis to keep up with the data growth rate. An experimental evaluation of insider threat detection methods is carried in this paper. Firstly, the open data set is divided by day and week. Then, various temporal representations including percentile, mean difference, median difference, and concatenation of instances representations, are used to extract potential information. Four unsupervised machine learning algorithms and a supervised algorithm are comprehensively compared to explore the best detection schemes under different temporal representations. To comprehensively evaluate the detection performance of the algorithms, four commonly used performance metrics (i.e. Detection rate (DR), Precision (PR), F1-score (F1), Area Under the Curve (AUC)) are introduced. The experimental results show that the random forest algorithm can achieve the best detection results using day granularity data combined with median differential representation-meddiff30, and the DR, PR, F1, and AUC are 75.43%, 99.59%, 85.84%, and 87.71% respectively. For the four unsupervised detection algorithms, under the 20% investigation budget, the autoencoder combined with percentile representation-percentile30 in day granularity achieved DR of 95.83%, and AUC of 94.93%, with higher PR and F1 values under the same conditions. The local outlier factor algorithm combined with concatenation representation-c3 under week granularity achieved the highest DR, but the scheme of the autoencoder combined with percentile30 still achieved the highest PR, F1, and AUC.

关键词： insider threats unsupervised algorithm supervised algorithm temporal representation performance metrics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：