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 vi...
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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.
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....
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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.
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 ...
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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-basedclustering 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.
To more accurately track multiple group targets in scenarios involving group conformity and group division, an improved cardinalized probability hypothesis density (ICPHD) based on density-basedspatialclustering of ...
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ISBN:
(纸本)9798400717178
To more accurately track multiple group targets in scenarios involving group conformity and group division, an improved cardinalized probability hypothesis density (ICPHD) based on density-based spatial clustering of applications with noise (ICPHD-DBSCAN) for multiple group targets tracking is proposed. The proposed algorithm addresses the issue of insufficient estimation accuracy due to the underutilization of measurement number information in traditional methods by offering new analytical solution. It considers the impact of measurement number on the group state of targets, effectively enhancing the accuracy and practicality of the estimation. Furthermore, to tackle the exponential partitioning problem that may arise in the GGIW-CPHD filter when dealing with a large number of measurements, the proposed algorithm incorporates an innovative method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. This not only significantly reduces the number of partitions to be considered but also accurately distinguishes measurements when targets are in group conformity and group division, thereby markedly improving the performance and precision of multi-target tracking. Simulation results demonstrate that the proposed algorithm is effective for tracking multiple group targets in scenarios with group conformity and group division.
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...
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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.
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 res...
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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.
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...
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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.
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 Pa...
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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.
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 s...
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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.
In contrast to daily travel behaviour, long-distance mobility constitutes a poorly understood area in transport research. Only few national household travel surveys include sections on long-distance travel and these u...
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In contrast to daily travel behaviour, long-distance mobility constitutes a poorly understood area in transport research. Only few national household travel surveys include sections on long-distance travel and these usually focus on the trip to the destination without gathering information about mobility behaviour at the destination. Other sources of data on mobility are either restricted to the national level such as cell phone data or to specific modes of transport such as international flight statistics or floating car data. In addition, the outbreak of the COVID-19 pandemic in 2020 has illustrated how difficult it is to grasp abrupt changes in mobility behaviour. Against this background this paper investigates the potential of Flickr data for capturing patterns and radical changes in long-distance mobility. Flickr is a social media online platform allowing its users to upload photos and to comment on their own and other users’ photos. It is mainly used for sharing holiday and travel experiences. The results show that Flickr constitutes a viable source of data for capturing patterns and radical changes in long-distance mobility. The distribution of the travel distances, the travel destinations as well as reduction of the mileage of all holiday trips in 2020 in comparison to 2019 due to the pandemic calculated on the basis of the Flickr data is very similar to the same indicators determined on the basis of a national household travel survey, official passenger flight statistics, and other official transportation statistics.
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