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PSO-aided fuzzy inference of material elastic constants with resonant ultrasound spectroscopy

INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2021年第10期29卷 1429-1444页

作者： Yang, Kai Liu, Jinbo Zhu, Tao Wang, Hui Zhu, Xinxin China Aerodynam Res & Dev Ctr CARDC Hyperveloc Aerodynam Inst Mianyang 621000 Sichuan Peoples R China

Fuzzy inference method is applied to formulate an algorithm capable of estimating material elastic constants (ECs) of a specimen by solving an inverse problem with a group of measured resonance frequencies obtained via Resonant Ultrasound Spectroscopy (RUS). The algorithm is validated with RUS data from a specimen of polycrystalline aluminium alloy. Then the algorithm is found to be sensitive to the initial ECs by processing RUS data from a specimen of fine-grain polycrystalline Ti-6Al-4V, the same as the Levenberg-Marquardt (L-M) method popularly used in solving inverse problems. To overcome such a drawback, a hybrid method of Particle Swarm Optimization (PSO) and density-based spatial clustering of applications with noise (DBSCAN) is proposed. And it is used to generate several groups of initial ECs for the fuzzy inference method. There is a trade-off between computational time and accurately estimated ECs, since the hybrid method needs more time to directly find out accurate ECs.

关键词： Elastic constant fuzzy inference resonant ultrasound spectroscopy inverse problem particle swarm optimization density-based spatial clustering of applications with noise

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A practical building energy consumption anomaly detection method based on parameter adaptive setting DBSCAN

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COGNITIVE COMPUTATION AND SYSTEMS 2021年第2期3卷 154-168页

作者： Yao, Gang Guo, Chen Ge, Quanbo Ait-Ahmed, Mourad Shanghai Maritime Univ Sino Dutch Mech Engn Dept Shanghai Peoples R China AVIC Beijing Shuguang Aviat Elect Co Ltd Beijing Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai Peoples R China Univ Nantes IREENA Polytech Nantes Nantes France

In order to realize the Building Energy Consumption Anomaly Detection (BECAD) for the green building assessment, the density-based spatial clustering of applications with noise (DBSCAN) is adopted for data clustering. To deal with the parameter setting difficulty of the DBSCAN, a practical parameter adaptive setting method is proposed. The presented method determines values of the DBSCAN parameters, MinPts and epsilon, according to four distribution characteristics (average data distance, data local densities, cosine similarity, and equivalent space radius) of data, and does not need prior knowledge of the datasets. Furthermore, parameter values determined by the proposed method can improve the clustering effect of the DBSCAN on datasets with various data densities. After testing the proposed method with open datasets, DBSCAN with the parameter adaptive setting method is applied to the BECAD. Experiment results show that identified building energy utilization patterns and abnormal buildings are reasonable and the results can offer the management departments a clear understanding of building energy consumption patterns, as well as decision supports to make subsequent improvement measures.

关键词： data local densities parameter values data densities parameter adaptive setting method building energy utilization patterns clustering effect practical building energy consumption anomaly detection method density-based spatial clustering of applications with noise pattern clustering energy consumption power engineering computing data clustering data analysis data mining DBSCAN parameters BECAD building management systems data distance green building assessment energy consumption patterns parameter adaptive setting DBSCAN

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K-means Find density Peaks in Molecular Conformation clustering

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Chinese Journal of Chemical Physics 2022年第2期35卷 353-368,I0026-I0030,I0003页

作者： Guiyan Wang Ting Fu Hong Ren Peijun Xu Qiuhan Guo Xiaohong Mou Yan Li Guohui Li School of Information Engineering Dalian Ocean UniversityDalian 116029China Pharmacy Department of Affiliated Zhongshan Hospital of Dalian University Dalian 116001China State Key Laboratory of Molecular Reaction Dynamics Dalian Institute of Chemical PhysicsDalian 116023China Department of Ophthalmology Aerospace Center Hospital Beijing 10049China Liaoning Normal University Dalian 116023China

Performing cluster analysis on molecular conformation is an important way to find the representative conformation in the molecular dynamics ***,it is a critical step for interpreting complex conformational changes or interaction *** one of the density-based clustering algorithms,find density peaks(FDP)is an accurate and reasonable candidate for the molecular conformation ***,facing the rapidly increasing simulation length due to the increase in computing power,the low computing efficiency of FDP limits its application *** we propose a marginal extension to FDP named K-means find density peaks(KFDP)to solve the mass source consuming *** KFDP,the points are initially clustered by a high efficiency clustering algorithm,such as *** centers are defined as typical points with a weight which represents the cluster ***,the weighted typical points are clustered again by FDP,and then are refined as core,boundary,and redefined halo *** this way,KFDP has comparable accuracy as FDP but its computational complexity is reduced from O(n^(2))to O(n).We apply and test our KFDP method to the trajectory data of multiple small proteins in terms of torsion angle,secondary structure or contact *** comparing results with K-means and density-based spatial clustering of applications with noise show the validation of the proposed KFDP.

关键词： K-means find density peaks Molecular clustering density-based spatial clustering of applications with noise

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Drone assisted Multi sensory Autonomous Driving System

Drone assisted Multi sensory Autonomous Driving System

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第三十一届中国汽车工程学会年会暨展览会（SAECCE 2024）

作者： Zhang Bolun Zhuang Ao Liu Yanbo Han Zeyuan Sun Fuyao Chen Linxianyi Pan Zhenyue Guo Jianmei Xu Wenchao Shen Zanwei Song Libo Li Zhengyu Zhang Zhiyong Dan Yilin Chen Yancheng Sun Weiqi Ji Yifan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Data Science & Engineering East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Intel(China)Ltd.Network and Edge Group(China) Intel(China)Ltd.Data Center and AI Group(China) STMicroelectronics Zhengzhou University

Autonomous driving has experienced rapid development in the past decade. Considering that relying solely on onboard sensors limits the safety and efficiency of autonomous driving, introducing a multi-sensory system to perceive road conditions is an effective solution, with roadside units being a typical attempt of a multi-sensory autonomous driving system. However, roadside systems have issues such as high cost, long construction cycles, low coverage, and low short-tenn utilization rates. Therefore, we propose the Drone-Assisted Multi-Sensory Autonomous Driving System (DAMAD). DAMAD consists of a vehicle perception system, a blind spot detection system, a drone control system, a drone perception system, and an autonomous driving decision system. This paper focuses on the methods of implementing the blind spot identification system and drone sensing system and verifies the performance improvement of DA-MAD on DDQN-based deep learning autonomous driving decision systems in a simulation scenario of a typical bidirectional dual-lane multi-intersection. Simulation results show that DAMAD can significantly improve the safety and efficiency of the autonomous driving decision system under ideal conditions. Additionally, we conducted real-vehicle tests, which demonstrated that DAMAD could help autonomous vehicles perceive obscured environmental information in advance, assisting the autonomous driving system in addressing emergencies.

关键词： autonomous driving drone control DDQN density-based spatial clustering of applications with noise

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COVID-19 Monitoring System using Social Distancing and Face Mask Detection on Surveillance video datasets

COVID-19 Monitoring System using Social Distancing and Face ...

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3rd IEEE International Conference on Emerging Smart Computing and Informatics (IEEE-ESCI)

作者： Srinivasan, Sahana Singh, Rujula R. Biradar, Ruchita R. Revathi, S. A. RV Coll Engn Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9781728185194

In the current times, the fear and danger of COVID-19 virus still stands large. Manual monitoring of social distancing norms is impractical with a large population moving about and with insufficient task force and resources to administer them. There is a need for a lightweight, robust and 24X7 video-monitoring system that automates this process. This paper proposes a comprehensive and effective solution to perform person detection, social distancing violation detection, face detection and face mask classification using object detection, clustering and Convolution Neural Network (CNN) based binary classifier. For this, YOLOv3, density-based spatial clustering of applications with noise (DBSCAN), Dual Shot Face Detector (DSFD) and MobileNetV2 based binary classifier have been employed on surveillance video datasets. This paper also provides a comparative study of different face detection and face mask classification models. Finally, a video dataset labelling method is proposed along with the labelled video dataset to compensate for the lack of dataset in the community and is used for evaluation of the system. The system performance is evaluated in terms of accuracy, F1 score as well as the prediction time, which has to be low for practical applicability. The system performs with an accuracy of 91.2% and F1 score of 90.79% on the labelled video dataset and has an average prediction time of 7.12 seconds for 78 frames of a video.

关键词： COVID-19 density-based spatial clustering of applications with noise You Only Look Once Dual Shot Face Detector MobileNetV2 Face detection Convolution Neural Networks Face Mask Classification Social Distancing Video surveillance

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Multi-Sound-Source Localization Using Machine Learning for Small Autonomous Unmanned Vehicles with a Self-Rotating Bi-Microphone Array

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2021年第3期103卷 52-52页

作者： Gala, Deepak Lindsay, Nathan Sun, Liang New Mexico State Univ Klipsch Sch Elect & Comp Engn Las Cruces NM 88001 USA New Mexico State Univ Dept Mech & Aerosp Engn Las Cruces NM 88001 USA

While vision-based localization techniques have been widely studied for small autonomous unmanned vehicles (SAUVs), sound-source localization capabilities have not been fully enabled for SAUVs. This paper presents two novel approaches for SAUVs to perform three-dimensional (3D) multi-sound-sources localization (MSSL) using only the inter-channel time difference (ICTD) signal generated by a self-rotating bi-microphone array. The proposed two approaches are based on two machine learning techniques viz., density-based spatial clustering of applications with noise (DBSCAN) and Random Sample Consensus (RANSAC) algorithms, respectively, whose performances were tested and compared in both simulations and experiments. The results show that both approaches are capable of correctly identifying the number of sound sources along with their 3D orientations in a reverberant environment.

关键词： Sound source localization Machine learning Random sample consensus RANSAC density-based spatial clustering of applications with noise DBSCAN Multi-sound-source localization Small autonomous unmanned vehicles (SAUVs) Bi-microphone array Three-dimensional (3D) multi-sound-source localization

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An Iterated Local Search-based Algorithm to Support Cell Nuclei Detection in Pap Smears Test 1

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21st International Conference on Enterprise Information Systems (ICEIS)

作者： Diniz, Debora N. Souza, Marcone J. F. Carneiro, Claudia M. Ushizima, Daniela M. de Medeiros, Fatima N. S. Oliveira, Paulo H. C. Bianchi, Andrea G. C. Univ Fed Ouro Preto Inst Exact Sci & Biol Grad Program Comp Sci Ouro Preto Brazil Univ Fed Ouro Preto Biol Sci Res Ctr Grad Program Biotechnol Ouro Preto Brazil Univ Calif Berkeley Berkeley Inst Data Sci Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Berkeley CA USA Univ Fed Ceara Teleinformat Engn Dept Fortaleza Ceara Brazil

ISBN: (数字)9783030407834

ISBN: (纸本)9783030407834;9783030407827

The focus of this work is on the detection of nuclei in synthetic images of cervical cells. Finding nuclei is an important step in building a computational method to help cytopathologists identify cell changes from Pap smears. The method developed in this work combines both the Multi-Start and the Iterated Local Search metaheuristics and uses the features of a region to identify a nucleus. It aims to improve the assertiveness of the screening and reduce the professional workload. The irace package was used to automatically calibrate all parameter values of the method. The proposed approach was compared with other methods in the literature according to recall, precision, and F1 metrics using the ISBI Overlapping Cytology Image Segmentation Challenge database (2014). The results show that the proposed method has the second-best values of F1 and recall, while the accuracy is still high.

关键词： Nuclei segmentation Cervical cells Simple linear iterative clustering density-based spatial clustering of applications with noise Iterated Local Search Multi-Start Metaheuristic Pap smear images analysis

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Hybrid Model for Cleaning Abnormal Data of Wind Turbine Power Curve based on Machine Learning Approaches

e-Prime - Advances in Electrical Engineering, Electronics an...

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2025年

作者： Abdelwahab Ayash Subuh S. Hr. Aghay Kaboli Muhammad Waqar François Vallée Power Systems and Markets Research Group University of Mons 7000 Mons Belgium State company of electricity production/Northern region Iraq

This paper addresses important challenges in wind energy prediction caused by outliers in wind data, which distort the wind turbine power curve and lead to inaccurate performance assessments and suboptimal operation strategies. The major difficulty here is detecting and eliminating these outliers from complex wind datasets, as inaccurate data can significantly impact forecasting and related activities. To overcome this challenge, the paper proposes a hybrid model combining fuzzy C-means clustering, Mahalanobis distance, and Artificial Neural Networks (ANN) to detect and remove outliers far more accurately than any individual method or other traditional hybrid method, decreasing false alarms and misses. It improves data quality and boosts the reliability of turbine performance analysis, resource assessment, and forecasting, supporting more efficient and sustainable wind-power operations. The results show (1) that the proposed hybrid model achieves 15.4% more accuracy than the other traditional hybrid models in detecting and removing outliers. (2) The proposed hybrid model gives an overall ≈ 116.1% improvement in outlier-detection accuracy over the individual models. (3) Adding the ANN to the proposed hybrid model boosts the outlier-detection accuracy to about a 69.5% relative improvement. (4) Detecting and cleaning outliers by the proposed hybrid model cuts the RMSE from 2.38 to 1.27, reducing prediction error by 46.6%. (5) The advanced hybrid model used in this study for comparison purposes achieves nearly identical accuracy to the proposed hybrid model; it reduces RMSE by ∼0.015 and MAPE by ∼0.04 pp and boosts R² by ∼0.001 while maintaining almost perfect outlier detection (99 % vs. 100 %). Although the advanced model offers a marginal edge in reconstruction quality, the lightweight, scalable proposed hybrid model remains better appropriate for real-world deployment due to its lower computational overhead and more straightforward maintenance.

关键词： Wind turbine power curve outliers fuzzy c-means clustering Mahalanobis distance Artificial Neural Networks support vector regression ANFIS K-means RMSE MAPE WTPC wind turbine power curve SCADA Supervisory Control and Data Acquisition FCM fuzzy c-means clustering ANFIS adaptive neuro-fuzzy interference system ANN artificial neural network SVR support vector regression MAE mean absolute error RMSE root mean square error R 2 coefficient of determination MAPE mean absolute percentage error DAE Denoising Autoencoder LSTM A Long Short-Term Memory Autoencoder DBSCAN density-based spatial clustering of applications with noise

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Wind power forecasting based on a novel gated recurrent neural network model

Wind Energy and Engineering Research

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Wind Energy and Engineering Research 2024年 1卷

作者： Shuo Zhang Emma Robinson Malabika Basu Technological University Dublin Dublin Ireland

Accurate wind power forecasting is crucial for upgrading the operation and maintenance of wind turbines and the effectiveness in wind power dispatching. In this work, density-based spatial clustering of applications with noise (DBSCAN) is adopted to exclude outliers, and is examined to yield top-quality data filtering for neural networks when implemented on supervisory control and data acquisition (SCADA) data from a 7-MW wind turbine. Apart from features, such as wind and rotor speeds, time is a vital feature involved with synchronous or asynchronous events. Thus, a model-agnostic vector representation for time, Time2Vec (T2V), is used. This can be embedded with neural network architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The novel neural network model, T2V-GRU, concatenates T2V into vanilla GRU, replacing the notion of time vector by T2V. Forecasting results are evaluated by mean absolute error (MAE), root mean squared error (RMSE), and accuracy. In comparison to T2V-LSTM, LSTM and GRU, the proposed T2V-GRU model in combination with DBSCAN refinement has superior forecasting results for the 7-MW turbine, yielding maximum forecasting accuracy (93.12 %) and minimal prediction errors in terms of MAE (286.10 kW) and RMSE (489.54 kW), while simultaneously reducing the computational cost and time.

关键词： ANN Artificial Neural Networks DBSCAN density-based spatial clustering of applications with noise GRU Gated Recurrent Unit IF Isolation Forest LSTM Long Short-Term Memory MAE Mean Absolute Error MSE Mean Squared Error NWP Numerical Weather Prediction ORE Offshore Renewable Energy PMG Permanent Magnet Generator R 2 R-square ReLU Rectified Linear Unit RMSE Root Mean Squared Error RNN Recurrent Neural Network SCADA Supervisory Control and Data Acquisition T2V Time2Vec WT Wind Turbine density-based spatial clustering of applications with noise (DBSCAN) Supervisory Control and Data Acquisition (SCADA) Time2Vec (T2V) Gated Recurrent Unit (GRU) T2V-GRU

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The Millimeter-Wave Radar SLAM Assisted by the RCS Feature of the Target and IMU

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SENSORS 2020年第18期20卷 5421.-5421.页

作者： Li, Yang Liu, Yutong Wang, Yanping Lin, Yun Shen, Wenjie North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China

Compared with the commonly used lidar and visual sensors, the millimeter-wave radar has all-day and all-weather performance advantages and more stable performance in the face of different scenarios. However, using the millimeter-wave radar as the Simultaneous Localization and Mapping (SLAM) sensor is also associated with other problems, such as small data volume, more outliers, and low precision, which reduce the accuracy of SLAM localization and mapping. This paper proposes a millimeter-wave radar SLAM assisted by the Radar Cross Section (RCS) feature of the target and Inertial Measurement Unit (IMU). Using IMU to combine continuous radar scanning point clouds into "Multi-scan," the problem of small data volume is solved. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is used to filter outliers from radar data. In the clustering, the RCS feature of the target is considered, and the Mahalanobis distance is used to measure the similarity of the radar data. At the same time, in order to alleviate the problem of the lower accuracy of SLAM positioning due to the low precision of millimeter-wave radar data, an improved Correlative Scan Matching (CSM) method is proposed in this paper, which matches the radar point cloud with the local submap of the global grid map. It is a "Scan to Map" point cloud matching method, which achieves the tight coupling of localization and mapping. In this paper, three groups of actual data are collected to verify the proposed method in part and in general. based on the comparison of the experimental results, it is proved that the proposed millimeter-wave radar SLAM assisted by the RCS feature of the target and IMU has better accuracy and robustness in the face of different scenarios.

关键词： millimeter-wave radar simultaneous localization and mapping radar cross section density-based spatial clustering of applications with noise correlative scan matching

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