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Rapid Method of Wastewater Classification by Electronic Nose for Performance Evaluation of Bioreactors with Activated Sludge

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SENSORS 2023年第20期23卷 8578-8578页

作者： Pilat-Rozek, Magdalena Dziadosz, Marcin Majerek, Dariusz Jaromin-Glen, Katarzyna Szelag, Bartosz Guz, Lukasz Piotrowicz, Adam Lagod, Grzegorz Lublin Univ Technol Fac Math & Informat Technol PL-20618 Lublin Poland Polish Acad Sci Inst Agrophys PL-20290 Lublin Poland Warsaw Univ Life Sci Inst Environm Engn SGGW PL-02797 Warsaw Poland Lublin Univ Technol Fac Environm Engn PL-20618 Lublin Poland

Currently, e-noses are used for measuring odorous compounds at wastewater treatment plants. These devices mimic the mammalian olfactory sense, comprising an array of multiple non-specific gas sensors. An array of sensors creates a unique set of signals called a "gas fingerprint", which enables it to differentiate between the analyzed samples of gas mixtures. However, appropriate advanced analyses of multidimensional data need to be conducted for this purpose. The failures of the wastewater treatment process are directly connected to the odor nuisance of bioreactors and are reflected in the level of pollution indicators. Thus, it can be assumed that using the appropriately selected methods of data analysis from a gas sensors array, it will be possible to distinguish and classify the operating states of bioreactors (i.e., phases of normal operation), as well as the occurrence of malfunction. This work focuses on developing a complete protocol for analyzing and interpreting multidimensional data from a gas sensor array measuring the properties of the air headspace in a bioreactor. These methods include dimensionality reduction and visualization in two-dimensional space using the principal component analysis (PCA) method, application of data clustering using an unsupervised method by Density-Based Spatial Clustering of Applications with Noise (dbscan) algorithm, and at the last stage, application of extra trees as a supervised machine learning method to achieve the best possible accuracy and precision in data classification.

关键词： electronic nose multidimensional data analysis principal component analysis dbscan algorithm extra trees wastewater classification performance evaluation activated sludge

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Twitter emergency response system during flood-related disaster

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INTERNATIONAL JOURNAL OF EMERGENCY MANAGEMENT 2021年第2期17卷 177-193页

作者： Singh, Jagrati Singh, Anil Kumar Motilal Nehru Natl Inst Technol Allahabad Dept Comp Sci Engn Allahabad 211004 Uttar Pradesh India

The ubiquity of mobile, laptop and social media platforms such as Twitter is accelerating the information flow around the world. This can be useful to handle emergencies during a natural disaster, as victims can share on-the-spot situations and officials can put the solutions on the same platform. We proposed a new emergency response system, aimed at providing help to victims during flood-related disasters based on their tweets. The system consists of three modules: The first module identifies high priority tweets that are asking for help. In the second module, each high priority tweet is given an emergency score based on the victim's location information to help them at an early stage. We proposed a new natural language technique named LDFT (location detection from a tweet) to infer the mentioned locations in the text of the tweet. The third module applies the dbscan clustering algorithm on high scoring tweets and displays the obtained clusters on Google Maps to visualise the hot-spot areas easily. The proposed disaster response system works well, with its maximum accuracy of 78%, and location prediction accuracy of 75%.

关键词： Twitter stream dbscan algorithm location prediction emergency-score classification clustering

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Point cloud voxel classification of aerial urban LiDAR using voxel attributes and random forest approach

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INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 2023年 118卷

作者： Aljumaily, Harith Laefer, Debra F. Cuadra, Dolores Velasco, Manuel Carlos III Univ Madrid Dept Comp Sci & Engn Ave Univ 30 Madrid 28911 Spain NYU Ctr Urban Sci & Progress Tandon Sch Engn 370 Jay St Brooklyn NY 11201 USA NYU Tandon Sch Engn Dept Civil & Urban Engn 370 Jay St Brooklyn NY 11201 USA King Juan Carlos Univ Dept Comp Sci & Stat Madrid Spain

The opportunities now afforded by increasingly available, dense, aerial urban LiDAR point clouds (greater than100 pts/m2) are arguably stymied by their sheer size, which precludes the effective use of many tools designed for point cloud data mining and classification. This paper introduces the point cloud voxel classification (PCVC) method, an automated, two-step solution for classifying terabytes of data without overwhelming the computational infrastructure. First, the point cloud is voxelized to reduce the number of points needed to be processed sequentially. Next, descriptive voxel attributes are assigned to aid in further classification. These attributes describe the point distribution within each voxel and the voxel's geo-location. These include 5 pointdescriptors (density, standard deviation, clustered points, fitted plane, and plane's angle) and 2 voxel position attributes (elevation and neighbors). A random forest algorithm is then used for final classification of the object within each voxel using four categories: ground, roof, wall, and vegetation. The proposed approach was evaluated using a 297,126,417 point dataset from a 1 km2 area in Dublin, Ireland and 50% denser dataset of New York City of 13,912,692 points (150 m2). PCVC's main advantage is scalability achieved through a 99 % reduction in the number of points that needed to be sequentially categorized. Additionally, PCVC demonstrated strong classification results (precision of 0.92, recall of 0.91, and F1-score of 0.92) compared to previous work on the same data set (precision of 0.82-0.91, recall 0.86-0.89, and F1-score of 0.85-0.90).

关键词： Object Classification MapReduce Big Data LiDAR dbscan algorithm RANSAC algorithm

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Running space performance evaluation based on machine learning algorithms: A case study of the central districts of Kunming 24

Running space performance evaluation based on machine learni...

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Proceedings of the 2024 4th International Conference on Big Data, Artificial Intelligence and Risk Management

作者： Wenrui Zou Kunming University of Science and Technology Kunming Yunnan China

ISBN: (纸本)9798400709753

Amidst a national fitness initiative, urban running spaces in China often suffer from a lack of natural appeal, inadequate infrastructure, and insufficient vitality. To address these issues and offer precise recommendations for plateau lakeside cities, this paper investigates running spaces and the application of machine learning algorithms, focusing on Kunming's central urban area using spontaneously provided running track data. By incorporating the dbscan algorithm into the clustering analysis of running activities and spaces, we identified significant findings: 1. There are considerable spatial differences in running preferences in Kunming's central area, 2. The area has an excellent natural foundation, which is underutilized by runners, and 3. There are substantial disparities in the supply and demand for running spaces across different streets, indicating supply issues. These insights aim to provide a scientific basis for improving the supply relationship between running activities and spaces, contributing to the development of future healthy cities.

关键词： dbscan algorithm

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Explore Hot Spots of City Based on dbscan algorithm

Explore Hot Spots of City Based on DBSCAN Algorithm

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International Conference on Audio, Language and Image Processing

作者： Xiaoqing Yu Yupu Ding Wanggen Wan Etienne Thuillier Institute of Smart City Shanghai University School of Communication and Information Engineering University of Technology of Belfort-Montbeliar

ISBN: (纸本)9781479939046

Spatial clustering is one of the main methods of data mining and knowledge discovery. dbscan algorithm can be found in space with "noise" database clustering of arbitrary shape, is a kind of good clustering algorithm. This paper introduces the basic concept and principle of dbscan algorithm, and applies this algorithm to perform clustering analysis distributions of weibo location information. The article compare k-means algorithm with dbscan algorithm in order to prove the effectiveness of dbscan algorithm.

关键词： Socialnetwork dbscan algorithm Data mining K-means algorithm

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Risk Assessment Method Combining Trajectory Prediction and Lateral Obstacle Monitoring 9

Risk Assessment Method Combining Trajectory Prediction and L...

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9th International Conference on Traffic and Logistic Engineering (ICTLE)

作者： Yang Wendi Xin Yahang Zhang Xiaoyu Lou Jiaming Wang Tianchen Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Tongji Univ Sch Automot Studies Shanghai Peoples R China

ISBN: (纸本)9781665427524

This paper carried out the design of the relevant part of the lidar of an enhanced two-way safety assistance system for commercial vehicles, which aims to provide real-time pedestrian position data for ordinary trucks or large trucks when turning right, and calculate the cluster centers formed by pedestrians and vehicles. The positional relationship of the dangerous area formed when turning is used to judge the dangerous situation of the pedestrian's position, to remind the driver to reduce the effect of traffic accidents.

关键词： Lidar visualization clustering dbscan algorithm

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Subset simulation with adaptable intermediate failure probability for robust reliability analysis: an unsupervised learning-based approach

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2022年第6期65卷 172-172页

作者： Zhao, Yinghao Wang, Zeyu South China Univ Technol Sch Civil Engn & Transportat Guangzhou 510641 Peoples R China Guangzhou Inst Bldg Sci Grp Co Ltd Guangzhou 510440 Peoples R China Tsinghua Univ Dept Civil Engn Beijing 100084 Peoples R China

Subset simulation (SS) was known for its computational efficiency in estimating small failure probabilities as well as reducing emulation demands. The main idea behind SS lies in decomposing the original reliability problem into sub-reliability problems with more frequent probabilities. By taking advantage of Markov Chain Monte Carlo-based sampling technique (MCMC), this innovative strategy enables the computational feasibility for estimating small failure probability based on the prescribed number of simulations. However, SS still has several limitations that can potentially decrease its computational efficiency. It is mainly attributed to the fact that the estimated failure probability can be inconsistent due to insufficient samples in each subset. Moreover, SS requires empirical definition for MCMC proposal sampling to guarantee a good acceptance rate. However, the inherent shortcoming of MCMC brings the correlations among the samples in each subset, which can inevitably cause the biased estimate for failure probability. To address these limitations, a new method, called ULSS, that combines SS, importance sampling (IS), and dbscan algorithm is proposed to overcome these limitations. Specifically, the batch size of samples in each subset is adaptively increased until the prescribed threshold is satisfied, which facilitates the adjustment of intermediate failure probabilities. To enable the process of adaptive sampling, MCMC is substituted with IS to draw samples located in the effective sampling regions defined through dbscan. Computational performance of ULSS is demonstrated by investigating three examples with one for illustrative interpretation and the other two for engineered paradigm. Results indicate the computational consistency, unbias, and robustness of ULSS in terms of the statistical properties of the estimated failure probability.

关键词： Reliability analysis Monte carlo simulation Subset simulation Importance sampling Unsupervised learning dbscan algorithm

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Cross-entropy-based directional importance sampling with von Mises-Fisher mixture model for reliability analysis

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2022年第0期220卷 108306-108306页

作者： Zhang, Xiaobo Lu, Zhenzhou Cheng, Kai Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

In structural reliability analysis, the estimation of rare failure probability is a significant challenge. Directional importance sampling may address this challenge when a quasi-optimal sampling density is chosen. Directional importance sampling involves sampling on the unit hypersphere and one-dimensional reliability analysis. This paper develops a cross-entropy-based directional importance sampling (CE-DIS) for reliability analysis. The von Mises-Fisher mixture (vMFM) model is selected as sampling density to generate direction vectors on the unit hypersphere. The updating rules based on the cross-entropy method are derived for the vMFM model to find a quasi-optimal sampling density. In addition, the dbscan algorithm is used to choose the number of distributions in the vMFM model in case of no prior information of failure domain, and the intermediate failure event is introduced in case of not enough samples to update the distribution parameters. Several benchmark numerical examples and engineering applications are used to test the performance of the proposed CE-DIS method. The results demonstrate that the CE-DIS method significantly improves the computational efficiency compared to original directional sampling and other CE-based importance sampling methods. In addition, the CE-DIS method can be applied to different types of reliability problems, e.g., multiple design points, multiple failure modes and rare failure probability.

关键词： Directional importance sampling Cross-entropy von Mises-Fisher mixture dbscan algorithm Multiple design points

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Automatic modal parameter identification of high arch dams:feasibility verification

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Earthquake Engineering and Engineering Vibration 2020年第4期19卷 953-965页

作者： Li Shuai Pan Jianwen Luo Guangheng Wang Jinting Department of Hydraulic Engineering Tsinghua UniversityBeijing 100084China

Modal parameters, including fundamental frequencies, damping ratios, and mode shapes, could be used to evaluate the health condition of structures. Automatic modal parameter identification, which plays an essential role in realtime structural health monitoring, has become a popular topic in recent years. In this study, an automatic modal parameter identification procedure for high arch dams is proposed. The proposed procedure is implemented by combining the densitybased spatial clustering of applications with noise(dbscan) algorithm and the stochastic subspace identification(SSI). The 210-m-high Dagangshan Dam is investigated as an example to verify the feasibility of the procedure. The results show that the dbscan algorithm is robust enough to interpret the stabilization diagram from SSI and may avoid outline modes. This leads to the proposed procedure obtaining a better performance than the partitioned clustering and hierarchical clustering algorithms. In addition, the errors of the identified frequencies of the arch dam are within 4%, and the identified mode shapes are in agreement with those obtained from the finite element model, which implies that the proposed procedure is accurate enough to use in modal parameter identification. The procedure is feasible for online modal parameter identification and modal tracking of arch dams.

关键词： automatic modal parameter identification high arch dam dbscan algorithm stochastic subspace identification stabilization diagram ambient vibration

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An Experimental Comparison of algorithms for Nodes Clustering in a Neural Network of Caenorhabditis Elegans 21st

An Experimental Comparison of Algorithms for Nodes Clusterin...

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21st International Conference on Computational Science and Its Applications (ICCSA)

作者： Hernandez, Jorge Florez, Hector Univ Dist Francisco Jose de Caldas Bogota Colombia

ISBN: (纸本)9783030870133;9783030870126

Caenorhabditis Elegans (C. Elegans) is a worm, which has had several studies to search its nerve paths. In a neuronal network simulation, it is util to know which is the first weight to assign in each link if it is not presented to determine other characteristics (e.g. distances by weights). Normally, the weight is a heuristic to solve a problem. There is a data set about connections of C. Elegans which is a result of other authors. The weights by the connection are not set in the data set. In this work, we use the data set to determine experimental weights for each connection with four cluster algorithms. The weights are to use in future work. To compare the algorithms, we created several models for each algorithm. We used metrics to evaluate the results for each model. A spectral clustering algorithm was chosen how the better algorithm to generate the weights.

关键词： Clustering Caenorhabditis Elegans MiniBatch K-means algorithm dbscan algorithm Spectral clustering algorithm Clustering feature tree algorithm

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