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Shape investigations of structures formed by the self-assembly of aromatic amino acids using the density-based spatial clustering of applications with noise algorithm

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2022年第1期30卷 200-215页

作者： Habiboglu, M. Gokhan Hernandez, Helen W. Uyaver, Sahin Turkish German Univ Fac Engn Dept Elect & Elect Engn Istanbul Turkey KAL Res Initiat LLC Houston TX USA Turkish German Univ Fac Sci Dept Energy Sci & Technol Istanbul Turkey

Tyrosine, tryptophan, and phenylalanine are important aromatic amino acids for human health. If they are not properly metabolized, severe rare mental or metabolic diseases can emerge, many of which are not researched enough due to economic priorities. In our previous simulations, all three of these amino acids are discovered to be self-organizing and to have complex aggregations at different temperatures. Two of these essential stable formations are observed during our simulations: tubular-like and spherical-like structures. In this study, we develop and implement a clustering analyzing algorithm using density-based spatial clustering of applications with noise (DBSCAN) to measure the shapes of the formed structures by the self-assembly processes of these amino acids. We present the results in quantitative and qualitative ways. To the best of our knowledge, the geometric shapes of the formed structures by the self-assembly processes of these amino acids are not measured quantitatively in the literature. Analytical measurements and comparisons of these aggregations might help us to identify the self-aggregations quickly at early stages in our simulations and hence provide us with more opportunity to experiment with different parameters of the molecular simulations (like temperature, mixture rates, and density). We first implement the DBSCAN method to identify the main self-aggregation cluster and then we develop and implement two algorithms to measure the shapes of the formed structures by the self-assembly processes of these amino acids. The measurements, which are completely in line with our simulation results, are presented in quantitative and qualitative ways.

关键词： clustering analysis density-based spatial clustering of applications with noise self-assembly amino acids sphericity cylindricity

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An Improved Random Sample Consensus based on density-based spatial clustering of applications with noise for Image Mosaic

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2021年第4期31卷 625-631页

作者： Liu, Jinda Hou, Yanyang Pei, Hongxing Jilin Univ Sch Instrument Sci & Elect Engn Changchun 130061 Jilin Peoples R China Zhengzhou Univ Ind Technol Sch Informat Engn Xinzheng 451150 Henan Peoples R China Zhengzhou Univ Sch Phys & Engn Zhengzhou 450001 Henan Peoples R China

Image mosaic is the technique of constructing a sequence of images into a high-resolution image, which mainly includes image registration and image fusion. In this paper we propose a new method for image registration: feature vectors of matching points are formed firstly, then we use density-based spatial clustering of applications with noise to process feature vectors to improve Random Sample Consensus in the process of estimating transformation model between two images. The results show that proposed method outperforms the traditional method, which estimates the transformation model by random sample consensus only, on the spatial frequency, definition, and peak signal-to-noise ratio in images.

关键词： image registration random sample consensus density-based spatial clustering of applications with noise clustering

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LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise-genetic algorithm-radial basis function neural network

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020年第2期16卷

作者： Ren, Jingqiu Bao, Ke Zhang, Guanghua Chu, Li Lu, Weidang Northeast Petr Univ Sch Elect Engn & Informat Daqing 166318 Peoples R China Daqing Oilfield Informat Technol Co Daqing Peoples R China Zhejiang Univ Technol Coll Informat Engn Hangzhou Peoples R China

In recent years, fifth-generation communication technology has begun to experiment successfully. As an indoor positioning technology of the Internet of things, it changes with each passing day and shows great vitality in the development of smart cities. Aiming at the problem that existing radio frequency identification indoor positioning algorithm is prone to environmental interference and poor positioning accuracy, a LANDMARC indoor positioning algorithm based on density-based spatial clustering of applications with noise-genetic algorithm-radial basis function neural network is proposed. In this article, the signal intensity value is processed by Gaussian filter, and the noise points and boundary points are removed by density-based clustering algorithm. The threshold and weight of radial basis function neural network were optimized by genetic algorithm. With less data information, the relationship between the value of label signal strength and position coordinate could be established to improve the positioning accuracy of LANDMARC positioning algorithm. Experimental research shows that the average positioning error of the proposed LANDMARC algorithm based on density-based spatial clustering of applications with noise-genetic algorithm-radial basis function neural network is about 0.9 m, which is 64% lower than the average positioning error of the traditional LANDMARC algorithm and improves the indoor positioning accuracy.

关键词： LANDMARC density-based spatial clustering of applications with noise radial basis function neural network genetic algorithm smart city

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Aluminum alloy microstructural segmentation method based on simple noniterative clustering and adaptive density-based spatial clustering of applications with noise

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JOURNAL OF ELECTRONIC IMAGING 2019年第3期28卷 033035-033035页

作者： Zhang, Shiyue Chen, Dali Liu, Shixin Zhang, Pengyuan Zhao, Wei Northeastern Univ Coll Informat Sci & Engn Inst Artificial Intelligence & Robots Shenyang Liaoning Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Shandong Nanshan Aluminum Ind Co Ltd Res Inst Yantai Peoples R China

We propose an unsupervised segmentation method based on simple non-iterative clustering (SNIC) and adaptive density-based spatial clustering of applications with noise (DBSCAN). The method is not sensitive to parameter settings. And cluster parameter suitable for each image can be automatically calculated. SNIC superpixel segmentation is applied in achieving over-segmented images to solve the problem of the image resolution being too high. Then, adaptive DBSCAN clustering is proposed to cluster the over-segmented superpixel blocks to solve the problem of over-segmentation and manual adjustment of DBSCAN parameters. Finally, k-means and connected regions are used for postprocessing to remove the shadow superpixel blocks from the clustered image and to ensure the integrity of a single microstructure. The effectiveness of this method is proved by many experiments. based on this method, we provide a fast labeling method to help experts quickly label metallographic images. (C) 2019 SPIE and IS&T

关键词： aluminum alloy microstructure unsupervised segmentation simple non-iterative clustering density-based spatial clustering of applications with noise adaptive parameter

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Driver fixation region division-oriented clustering method based on the density-based spatial clustering of applications with noise and the mathematical morphology clustering

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ADVANCES IN MECHANICAL ENGINEERING 2015年第10期7卷 1-11页

作者： Li, Shi-wu Xu, Yi Sun, Wen-cai Yang, Zhi-fa Wang, Lin-hong Chai, Meng Wei, Xue-xin Jilin Univ Coll Transportat Changchun 130022 Peoples R China

A clustering method that combined density-based spatial clustering of applications with noise with mathematical morphology clustering is proposed to adapt to the features of driver's fixation such as points' dispersion and fixation regions' irregularity and solve the problems of conventional density-based spatial clustering of applications with noise method's large influence by parameters and mathematical morphology clustering's needs of much manual intervention. Drivers' fixation data were collected by Smart Eye Pro 5.7 eye tracking system, and the data were processed and clustered using conventional clustering methods and density-based spatial clustering of applications with noise-mathematical morphology clustering method. The results show that the method proposed in this article takes into account the advantages of density-based spatial clustering of applications with noise and mathematical morphology clustering to cluster irregular regions and makes up for defects of conventional clustering methods. It is verified that density-based spatial clustering of applications with noise-mathematical morphology clustering method is better than the conventional hierarchical clustering method and density-based spatial clustering of applications with noise method in driver's fixation points clustering and can improve the quality of driver's fixation region division.

关键词： Traffic engineering clustering density-based spatial clustering of applications with noise mathematical morphology clustering fixation region division crude boundary

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A feature sample screening and local outlier factor fusion method for detecting tidal stream turbine blade impact fault

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2025年第8期47卷 1594-1606页

作者： Wu, Zhen Wang, Tianzhen Cai, Youming Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China

The impact fault caused by ocean creatures poses a risk to the safe operation of tidal stream turbine blades. However, it is difficult to detect the weak impact fault directly because the collected signal is disturbed by the waves, turbulence, and continuously variable flow velocity. To solve this problem, a fusion method of feature sample screening and local outlier factor is proposed in this paper. This method consists of three main parts. First, the Teager-Kaiser energy operator and the sliding window technique are introduced to extract the envelope statistical features from the current signal. Second, a parameter optimized density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed to perform feature sample screening before detecting. Notably, the conventional DBSCAN algorithm is sensitive to the parameter selection and lacks the capability of adaptive screening, so this paper proposes an adaptive sand cat swarm optimization algorithm to optimize the parameters. Finally, the local outlier factor is utilized to detect faults based on the screened feature samples. The experimental results show that the proposed method stands out in reducing the false alarm rate compared with traditional methods. Specifically, within the flow velocity ranges of 1.0-1.3 m/s and 1.3-1.6 m/s, the false alarm rates can reach 0% and 0.17%, respectively.

关键词： Tidal stream turbine impact fault feature sample screening density-based spatial clustering of applications with noise adaptive sand cat swarm optimization local outlier factor

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Automated real-time prediction of geological formation tops during drilling operations: an applied machine learning solution for the Norwegian Continental Shelf

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JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2024年第6期14卷 1661-1703页

作者： Elahifar, Behzad Hosseini, Erfan Norwegian Univ Sci & Technol NTNU Dept Geosci & Petr Trondheim Norway NIOC Explorat Directorate Tehran Iran

Accurate prediction of geological formation tops is a crucial task for optimizing hydrocarbon exploration and production activities. This research investigates and conducts a comprehensive comparative analysis of several advanced machine learning approaches tailored for the critical application of geological formation top prediction within the complex Norwegian Continental Shelf (NCS) region. The study evaluates and benchmarks the performance of four prominent machine learning models: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest ensemble method, and Multi-Layer Perceptron (MLP) neural network. To facilitate a rigorous assessment, the models are extensively evaluated across two distinct datasets - a dedicated test dataset and a blind dataset independent for validation. The evaluation criteria revolve around quantifying the models' predictive accuracy in successfully classifying multiple geological formation top types. Additionally, the study employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm as a baseline benchmarking technique to contextualize the relative performance of the machine learning models against a conventional clustering approach. Leveraging two model-agnostic feature importance analysis techniques - Permutation Feature Importance (PFI) and Shapley Additive exPlanations (SHAP), the investigation identifies and ranks the most influential input variables driving the predictive capabilities of the models. The comprehensive analysis unveils the MLP neural network model as the top-performing approach, achieving remarkable predictive accuracy with a perfect score of 0.99 on the blind validation dataset, surpassing the other machine learning techniques as well as the DBSCAN benchmark. However, the SVM model attains superior performance on the initial test dataset, with an accuracy of 0.99. Intriguingly, the PFI and SHAP analyses converge in consistently pinpointing depth (DEPT), revolution per min

关键词： Geological formation tops prediction Machine learning Multi-layer perceptron Support vector machine K-nearest neighbors Random forest density-based spatial clustering of applications with noise

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A design and implementation of heart disease prediction model using data and ECG signal through hybrid clustering

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COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2023年第4期11卷 1532-1548页

作者： Sonawane, Ritesh Patil, Hitendra SSVPS BS Deore Coll Engn Comp Engn Dhule Maharashtra India

Detection of heart disease has become a significant topic in the medicalindustry while analyzing clinical information. Over the past years,heart disease diagnosis has been one of the emerging techniques indata mining. Besides this, developing an automated prediction systemis a challenging task, even when detecting the disease by ECG signaland data. Here, it proposes a flexible method to detect heart diseasewith the domain of both signal processing and data mining. Initially,the acquired features from the weighted feature extraction approachare fed into the hybrid clustering model;in turn, the two differentclustering results are taken to determine the finest output. Thehybrid clustering model is developed by K-Means clustering that issuperimposed with DBCAN, where the centroid and value are optimizedwith the aid of Modified Updating-based Chicken Swarm Optimization(MU-CSO). Simultaneously, the ECG signals are garnered and decomposedusing Discrete Wavelet Transform (DWT). Due to the curse ofdimensionality, the Principal Component Analysis (PCA) is ***, the MU-CSO-assisted hybrid clustering is employed todiagnose heart disease by ECG signal. Therefore, the efficacy of theprediction model is validated with various metrics and comparedagainst conventional methods.

关键词： Modified Updating-based Chicken Swarm Optimization K-means clustering density-based spatial clustering of applications with noise heart disease prediction

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A Novel Machine Learning Approach for Detecting Outliers, Rebuilding Well Logs, and Enhancing Reservoir Characterization

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NATURAL RESOURCES RESEARCH 2023年第3期32卷 1047-1066页

作者： Ali, Muhammad Zhu, Peimin Ma, Huolin Pan, Heping Abbas, Khizar Ashraf, Umar Ullah, Jar Jiang, Ren Zhang, Hao China Univ Geosci Inst Geophys & Geomat Wuhan Peoples R China China Univ Geosci Sch Econ & Management Wuhan Peoples R China Yunnan Univ Inst Int Rivers & Eco Secur Kunming 650500 Peoples R China Petro China Co Ltd Res Inst Petr Explorat & Dev Beijing Peoples R China

Irregular measurements may occur during the drilling process due to unconsolidated formation resulting in poor signal recordings by the logging tool. This affects the quality of data acquisition and the accuracy of elastic logs, such as density and velocity profiles, in reservoir characterization. It is of paramount importance to ensure the stability of the wireline-logging tool and to prevent compromising measurements of the formation's physical properties. While previous literature focused on the application of different machine learning (ML) algorithms for well logging, their application in a particular domain implied a narrow methodological utility for researchers. Therefore, this study combined two superior techniques of ML, supervised and unsupervised, for enhancing the elastic log response to ultimately help us to enhance reservoir characterization and interpretation. First, the density-based spatial clustering of applications with noise (DBSCAN) was used for outlier detection, and then, feature selection was used to identify highly correlated logs, which helped in rebuilding the density log. After successful ranking, the scaled-down features were carried forward to construct a regression model for density logs rebuilding. The comparative results confirmed the high accuracy of porosity estimated from rebuilt density log compared to that of core data. Consequently, it reduces cumbersome human efforts and time.

关键词： Machine learning density-based spatial clustering of applications with noise Detection of outliers Feature selections Well logs

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Transformer State Data Cleaning Method based on DBSCAN and ESN

Transformer State Data Cleaning Method Based on DBSCAN and E...

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IEEE/IAS Industrial and Commercial Power System Asia (IEEE I and CPS Asia)

作者： Li, Ke Zhang, Zhengdong Lu, Bo Kang, Jiayu Jia, Jinglong Zhang, Shenxi Sun, Tong State Grid Corp China East China Branch Shanghai Peoples R China Shanghai Jiao Tong Univ Key Lab Control Power Transmiss & Convers Minist Educ Shanghai Peoples R China

ISBN: (纸本)9798350339345

To address the problem of noise in transformer status monitoring data, this paper proposes a transformer status data cleaning method based on density-based spatial clustering of applications with noise (DBSCAN) and echo state network (ESN) to improve data quality. Firstly, all historical moment data are clustered by DBSCAN to get a number of normal clusters. Secondly, for the data to be cleaned that do not belong to the normal clusters, they are distinguished as device status abnormalities and sensor abnormalities according to this abnormality detection rule. Finally, the state parametric data due to sensor anomalies are repaired by using echo state network. The results show that this method can effectively detect the noise of transformer state parameter data, and the data repair effect is good, which verifies the effectiveness of this method.

关键词： transformer state covariate data data cleaning density-based spatial clustering of applications with noise echo state network

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