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density-based spatial clustering and discriminative modeling for automatic recognition and localization of cast-in hoist rings

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AUTOMATION IN CONSTRUCTION 2021年 125卷 103658-103658页

作者： Fang, Xin Jiang, Shaojie Zhang, Zongjun Wang, Ying Luo, Xiaochun Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Kowloon Hong Kong Peoples R China China State Construct Int Holdings Ltd China Overseas Bldg139 Hennessy Rd Hong Kong Peoples R China Shenzhen Hailong Construct Technol Co Ltd 8 Xingfa Rd Shenzhen Guangdong Peoples R China

Manual hooking the lifting gear of a crane onto the cast-in hoist rings (CHRs) of precast concrete components (PCCs) is the first step when loading PCCs for hoisting and transportation. However, the hooking process is dangerous and inefficient due to the complicated site environment and the falling risk workers may face when they need to work at height. This paper presents a method for automating the first step of the process: recognition and localization of CHRs of PCCs. We collect point clouds by placing laser radars on the surface of PCCs, and then the method follows three steps to implement the automation: denoising laser point clouds with a set of filtering rules, extracting rough recognition areas of CHRs using a spatial clustering algorithm, and determining the location of CHRs through pattern recognition. The experimental results indicate that the method achieves desired performance in terms of 0.864 precision and 0.822 recall.

关键词： Precast concrete component Automatic loading Laser scanning density-based spatial clustering Pattern recognition Automatic recognition and localization

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A Direction-of-Arrival Estimation Algorithm based on Compressed Sensing and density-based spatial clustering and Its Application in Signal Processing of MEMS Vector Hydrophone

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SENSORS 2021年第6期21卷 2191-2191页

作者： Yan, Huichao Chen, Ting Wang, Peng Zhang, Linmei Cheng, Rong Bai, Yanping North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China North Univ China Sch Sci Dept Math Taiyuan 030051 Peoples R China

Direction of arrival (DOA) estimation has always been a hot topic for researchers. The complex and changeable environment makes it very challenging to estimate the DOA in a small snapshot and strong noise environment. The direction-of-arrival estimation method based on compressed sensing (CS) is a new method proposed in recent years. It has received widespread attention because it can realize the direction-of-arrival estimation under small snapshots. However, this method will cause serious distortion in a strong noise environment. To solve this problem, this paper proposes a DOA estimation algorithm based on the principle of CS and density-based spatial clustering (DBSCAN). First of all, in order to make the estimation accuracy higher, this paper selects a signal reconstruction strategy based on the basis pursuit de-noising (BPDN). In response to the challenge of the selection of regularization parameters in this strategy, the power spectrum entropy is proposed to characterize the noise intensity of the signal, so as to provide reasonable suggestions for the selection of regularization parameters;Then, this paper finds out that the DOA estimation based on the principle of CS will get a denser estimation near the real angle under the condition of small snapshots through analysis, so it is proposed to use a DBSCAN method to process the above data to obtain the final DOA estimate;Finally, calculate the cluster center value of each cluster, the number of clusters is the number of signal sources, and the cluster center value is the final DOA estimate. The proposed method is applied to the simulation experiment and the micro electro mechanical system (MEMS) vector hydrophone lake test experiment, and they are proved that the proposed method can obtain good results of DOA estimation under the conditions of small snapshots and low signal-to-noise ratio (SNR).

关键词： direction of arrival estimation compressed sensing density-based spatial clustering MEMS vector hydrophone small snapshots low signal to noise ratio

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density-based Multimodal spatial clustering using Pre-trained Deep Network for Extracting Local Topics 5

Density-based Multimodal Spatial Clustering using Pre-traine...

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5th International ACM Workshop on Managing and Mining Enriched Geo-spatial Data (GeoRich)

作者： Sakai, Tatsuhiro Tamura, Keiichi Kitakami, Hajime Takezawa, Toshiyuki Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan Hiroshima Inst Technol Fac Informat Hiroshima Japan

ISBN: (纸本)9781450358323

Users on social networking services (SNSs) have been transmitting information about events they witnessed themselves in their daily life through geo-social data as geo-tagged texts and photos. Geo-social data are usually related to not only personal topics but also local topics and events. Therefore, extracting local topics and events in geo-social data is one of the most important challenges in many application domains. In this study, to extract local topics in geo-social data, we propose a new method based on a density-based multimodal spatial clustering algorithm called the (epsilon, sigma)-density-based multimodal spatial clustering, which can extract multimodal spatial clusters that are spatially and semantically separated from other spatial clusters. Moreover, to present the main topics of each multimodal spatial cluster, representative photos are detected using network-based importance analysis. The proposed method utilizes a pre-trained deep network for extracting feature vectors of photos, and feature vectors are utilized to calculate the similarity between two geo-social data. To evaluate our new local topic extraction method, we conducted experiments using actual geo-tagged tweets that include photos. The experimental results show that the proposed method can extract local topics as multimodal spatial clusters more sensitively than our previous method.

关键词： Multimodal clustering density-based spatial clustering Pre-trained deep network Topic extraction Geo-social data

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Enhanced copy-paste forgery detection in digital images using scale-invariant feature transform

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IET IMAGE PROCESSING 2020年第3期14卷 462-471页

作者： Selvaraj, Priyadharsini Karuppiah, Muneeswaran Mepco Schlenk Engn Coll Dept Comp Sci & Engn Sivakasi Tamil Nadu India

Image forgery detection and localisation is one of the principal problems in digital forensics. Copy-paste forgery in digital images is a type of forgery in which an image region is copied and pasted at another location within the same image. In this work, the authors propose a methodology to detect and localise copy-pasted regions in images based on scale-invariant feature transform (SIFT). Existing copy-paste forgery detection in images using SIFT and clustering techniques such as hierarchical agglomerative and density-based spatial clustering of applications with noise resulted many false pixel detections. They have introduced sensitivity-based clustering along with SIFT features to identify copy-pasted pixels and disregard the false pixels. Experimental evaluation on public image datasets MICC-F220, MICC-F2000 and MICC-F8 multi shows that the proposed method is showing improved performance in detecting and localising copy-paste forgeries in images than the existing works. Also the proposed work detects multiple copy-pasted regions in the images and is robust to attacks such as geometrical transformation of copied regions such as scaling and rotation.

关键词： copy protection pattern clustering feature extraction transforms image forensics object detection enhanced copy-paste forgery detection digital images digital forensics image region clustering techniques false pixel detections SIFT features copy-pasted pixels public image datasets MICC-F copied regions scale-invariant feature transform image forgery detection image forgery localisation density-based spatial clustering hierarchical agglomerative clustering MICC-F8 multidataset MICC-F2000 dataset multiple copy-pasted region detection sensitivity-based clustering

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Method of amplitude data recovery in PMU measurements that considers synchronisation errors

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第24期14卷 5746-5755页

作者： Xue, Ancheng Kong, He Lao, Yongzhao Xu, Feiyang Wang, Li Wei, Guoen Shi, Bonian Leng, Shuang Bi, Tianshu North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing 102206 Peoples R China Guangdong Power Grid Ltd Guangzhou Power Supply Bur Guangzhou 510000 Peoples R China Beijing Sifang Automat Co Ltd Beijing 100085 Peoples R China

The synchronised phasor measurement units (PMUs), serving as 'GridEye', provide data that improve the observability and controllability of the power grid due to its high accuracy and high-upload rate. However, missing or abnormal data may seriously affect its applications. This study proposes a method to recover the missing or abnormal amplitude data in PMU measurements (i.e. the active power, reactive power, positive sequence current, and voltage amplitude), based on the historical PMU data obtained from both ends of the line, which is independent of the transmission line parameters and the phase angle that may be influenced by synchronisation. First, the issues in the quality of measured PMU data are analysed, and the motivation of the proposed method is stated. Then, a method to screen out the bad data from the historical data is proposed based on the density-based spatial clustering of applications with noise. Furthermore, the model of data recovery is established, and the recovery method, which recovers the voltage amplitude, active power, reactive power, and current amplitude in sequence, using historical amplitude data to calculate related recovery coefficients is proposed. Finally, the effectiveness and practicability of the proposed method are verified by examples of simulation and measured data.

关键词： pattern clustering power grids power system state estimation phasor measurement synchronisation power system measurement power system control density-based spatial clustering recovery method voltage amplitude active power reactive power current amplitude historical amplitude data related recovery coefficients amplitude data recovery PMU measurements synchronisation errors synchronised phasor measurement units observability controllability power grid high-upload rate missing amplitude data abnormal amplitude data historical PMU data transmission line parameters measured PMU data bad data historical data

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Cluster division in wind farm through ensemble modelling

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IET RENEWABLE POWER GENERATION 2022年第7期16卷 1299-1315页

作者： Wang, Yue Li, Yong-Gang Xie, Huiyang Wu, Bin-Yuan Yang, Yi-Nan North China Elect Power Univ State Key Lab New Energy Power Syst Baoding Hebei Peoples R China Hanyang Univ Sch Elect & Engn Ansan South Korea State Grid Feidong Cty Power Supply Hefei Anhui Peoples R China

To increase the equivalence accuracy of wind farms and expand their applicability under multiple operating conditions, this paper proposes a method combining blending with extreme gradient boosting (XGBoost) to realize clustering index dimensionality reduction and dynamic time warping (DTW) optimization. density-based spatial clustering of applications with noise (DBSCAN) is aimed at realizing clustering and fusing the clustering results. This approach can help process multi-dimensional time-series feature operation data of wind turbines to formulate accurate and effective wind farm plans for the division of wind turbine clusters onsite. First, the XGBoost-Blending approach is used to select the clustering indicators for dimensionality reduction. Second, a clustering method based on DBSCAN-DTW is established to divide the clusters and perform ensemble clustering. Finally, MATLAB/Simulink is used to build a simulation model. In this manner, a three-phase short-circuit is introduced at the grid-connection point of a wind farm. A case study is performed under a variety of wind speed scenarios. The results verify the accuracy and wide applicability of the equivalence model formulated using the proposed method.

关键词： pattern clustering wind speed scenarios power engineering computing equivalence accuracy multiple operating conditions XGBoost-Blending approach dynamic time warping optimization ensemble clustering accurate wind farm plans wind turbine clusters time series clustering index dimensionality reduction DBSCAN-DTW ensemble modelling power grids cluster division equivalence model wind turbines clustering method multidimensional time-series feature operation data clustering indicators effective wind farm plans density-based spatial clustering wind power plants

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Vision-based detection and visualization of dynamic workspaces

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AUTOMATION IN CONSTRUCTION 2019年第Aug.期104卷 1-13页

作者： Luo, Xiaochun Li, Heng Wang, Hao Wu, Zezhou Dai, Fei Cao, Dongping Hong Kong Polytech Univ Dept Bldg & Real Estate Hung Hom Kowloon Hong Kong Peoples R China Cent Univ Finance & Econ Sch Management Sci & Engn Dept Construct Management 39 South Coll Rd Beijing 100081 Peoples R China Shenzhen Univ Coll Civil Engn Dept Construct Management & Real Estate 3688 Nanhai Ave Shenzhen 518060 Peoples R China West Virginia Univ Dept Civil & Environm Engn Morgantown WV 26506 USA Tongji Univ Sch Econ & Management Dept Construct Management & Real Estate 1239 Siping Rd Shanghai 200092 Peoples R China

Aligning workers and groups with workspaces in advance to enable peak performance and ensure safety is the essence of workspace planning. There are plans, while there have been few methods for examining these plans. We argue that capturing and visualizing actual dynamic workspaces is fundamental to plan examination. This paper describes an initial effort on integrating the latest computer vision methods to implement automatic detection and visualization of dynamic workspaces of workers on foot. To this end, object detection, multiple object tracking, and action recognition are adopted to collect two types of action data: action classes and action locations. A density-based spatial clustering algorithm is used to reason dynamic workspaces based on the action data. We evaluated each method of the integrated system and found that they have achieved the comparable performance in our envisaged settings with the original methods in their general settings. We also presented two demonstrations of the system. The research results represent an initial step towards developing a new management capability by capturing dynamic workspaces.

关键词： Dynamic workspaces Multiple object tracking Action recognition density-based spatial clustering

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Reducing dense local feature key-points for faster iris recognition

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COMPUTERS & ELECTRICAL ENGINEERING 2018年 70卷 939-949页

作者： Sahu, Beeren Sa, Pankaj Kumar Bakshi, Sambit Sangaiah, Arun Kumar Natl Inst Technol Dept Comp Sci & Engn Ctr Comp Vis & Pattern Recognit Rourkela 769008 Odisha India Vellore Inst Technol Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

Iris recognition has gained much attention in research and commercialization during the last decade. For a large population, the matching time of iris biometric system is much slower than the requirement. More the enrolled population size, higher the identification delay. To combat the delay without compromising accuracy of the system, the proposed method introduces a density-based spatial clustering and key point reduction to be applied on Phase Intensive Local Pattern (PILP) based dense feature extracted from the image. The reduction technique can also work with other dense local features. The reduction method is investigated whether it harms the accuracy of iris biometric system with respect to PILP. Widely used databases: BATH and CASIAv3 are used for experimentation. The technique is found successful in reducing representative key-points, thereby speeding up the match time up to five times. This improvement in 1:1 match-time is significant, and becomes more meaningful in identification for a large population. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Iris recognition Local feature Phase intensive local pattern density-based spatial clustering Feature reduction

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Robust Sparse and Dense Nonrigid Structure From Motion

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IEEE TRANSACTIONS ON MULTIMEDIA 2018年第4期20卷 841-850页

作者： Khan, Imran Ctr Adv Studies Engn Dept Comp Engn Islamabad 44000 Pakistan

Modeling deformable three-dimensional (3-D) shape from a video sequence is a fundamental task in computer vision. Nonrigid structure from motion (NRSfM) refers to the problem of recovering the 3-D shape and pose of an object, deforming over time from a monocular video sequence. Presently, dense NRSfM is a research problem of great interest in academia and the industry due to the large demand for 3-D data in various contexts. We provide a robust system for the sparse and dense NRSfM. The strength of our approach comes with the ability to deal with the trajectories corrupted with outliers that serve as an input to NRSfM. To tackle this problem, the input trajectories are processed with a density-based spatial clustering approach that is combined with a RANSAC technique for the outlier's detection. Processing the trajectories with this process enhances the trajectories by removing the unwanted outliers. Also, extending the work from sparse to dense NRSfM substantially increases the difficulty of the optimization problem. Thus, the proposed system also provides asymptotic improvements to the current optimization approaches by providing an efficient and a novel supervised Gauss Newton method. Extensive experiments have demonstrated that the proposed method outperforms most of the existing NRSfM methods. The results show that the proposed method reconstructs largely deforming objects accurately and efficiently.

关键词： Sparse and dense NRSfM density-based spatial clustering supervised Gauss-Newton

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An Evaluation Method for Pavement Maintenance Priority Classification based on an Unsupervised Data-Driven Multidimensional Performance Model

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022年第10期47卷 13265-13278页

作者： Zhao, Jing Wang, Xuancang Wang, Shuai Guo, Yucheng Ji, Guanyu Li, Shanqiang Changan Univ Highway Sch Xian 710064 Peoples R China Guangdong Hualu Transportat Co Ltd Highway Sch Guangzhou Peoples R China

To ensure pavement maintenance funds are reasonably allocated, it is necessary to comprehensively and effectively rank pavement sections based on multidimensional condition data to determine priority for rehabilitation. This paper proposes an unsupervised multidimensional performance data-driven model for evaluating road maintenance priority based on comprehensive multidimensional indicators, which solves the issues of a single decision index and inflexibility of traditional models. In this hybrid model, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is first used to cluster the similar pavement performance properties and identify noise in the data, then the support vector machine (SVM) is employed to assess the rank and priority of different pavement section clusters. Model's effectiveness was verified through a case study of a highway in Guangdong Province, China, in which data from 290 pavement sections were ranked according to five attributes. Six clusters and five maintenance levels were obtained, and the model was used to evaluate overall pavement performance and assign specific rehabilitation measures to different clusters. The results confirm that the proposed hybrid model requires almost no human intervention and can contribute to big data approaches to road maintenance management.

关键词： Road maintenance Pavement performance Evaluation model density-based spatial clustering Support vector machine

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