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14th International Space Syntax Symposium, SSS 2024

作者： Zhao, Gang Wu, Zhiqiang Ao, Xiang Shanghai Research Institute for Intelligent Autonomous Systems College of Architecture and Urban Planning Tongji University China College of Architecture and Urban Planning Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China Transport Studies Unit Oxford University United Kingdom

ISBN: (纸本)9791256690329

Space syntax has been an excellent tool for analyzing buildings' and cities' spatial configurations and social functions in recent decades. The topological thinking behind space syntax made it very convenient to incorporate numerous easy-to-use machine-learning techniques into the spatial network analysis framework. This paper proposed a new spatial structure analysis and synthesis paradigm, A-PageRank clustering, an angular-measure-based PageRank clustering algorithm concerning space syntax and graph analytics. The main idea is to use the measure of absolute angular between segments at the intersections as the random walk probability and thus employ the PageRank algorithm to derive a weighted adjacent matrix, then utilize clustering algorithms to reveal the hidden spatial structures of the whole urban space. This method was implemented during the Jinan City Territorial Planning to help identify spatial structures and planning zones. It’s recognized as a promising idea and exceedingly feasible for spatial structure explorations of urban areas considering both micro-level human perception and macro-level network structure, which was proved by real-world urban flow data. © 14th International Space Syntax Symposium, SSS 2024.

关键词： clustering algorithms

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AI Based Energy Efficient clustering in Homogeneous WSN 11

AI Based Energy Efficient Clustering in Homogeneous WSN

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11th International Conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Ranjan, Vaibhav Harsh Arunesh, Aditya Kumar Sharma, Richa Kaushik, Maunika Amity School of Engineering and Technology Amity University Department of Information Technology Uttar Pradesh Noida201313 India

ISBN: (纸本)9798350350357

Wireless sensor networks (WSNs) have motivated researchers to explore novel approaches to optimize cluster formation. clustering algorithms play a vital role in WSN s, wheresensor nodes are organized into clusters to reduce energy consumption and prolong network lifetime. This paper proposes an Al based energy-efficient clustering algorithm tailored for homogeneous clustering scenarios. The current research work leverages artificial intelligence techniques, such as ML. Optimization algorithms are used to dynamically form clusters based on the sensor nodes' energy levels and proximity. The key focus is on creating clusters with balanced energy distribution, which helps in avoiding the early depletion of energy in certain nodes and achieving a more uniform utilization of resources. The meaning of grouping calculations in WSNs couldn't possibly be more significant. These calculations should work out some kind of harmony between different clashing goals. On one hand, they should disseminate the bunch and make a beeline for guarantee even energy utilization among sensor hubs. On different, they should limit the above related with group development and support, as exorbitant bunching can prompt superfluous control message trades, accordingly, consuming extra energy. © 2024 IEEE.

关键词： clustering algorithms

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Multiple Sound Source Localization in Three Dimensions Using Convolutional Neural Networks and clustering Based Post-Processing

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IEEE ACCESS 2022年 10卷 125707-125722页

作者： Sakavicius, Saulius Serackis, Arturas Abromavicius, Vytautas Vilnius Tech Fac Elect Dept Elect Syst Vilnius 10221 Lithuania

Sound source localization methods are successfully applied for various estimation tasks, such as tracking and detecting objects, aiming cameras, and navigating robots. However, large and usually complex distributed microphone arrays are used for three-dimensional acoustic source localization. This study proposes a convolutional neural network architecture for three-dimensional sound source localization using a single tetrahedral microphone array. A spectrum phase component of a microphone array signal was designed as the input of the model, while the output represents a three-dimensional space. The paper provides extensive experimental results of the given method on a semi-synthetic audio data set and a real-world microphone array. Furthermore, cluster-based post-processing has been shown to increase the accuracy of three-dimensional localization by more than 30%. The experimental results on a synthesized data set using the image source method showed 1.08 m localization uncertainties. The estimate of the investigated sound sources had a mean absolute error of 18.97 & DEG;and elevation error of 48.49 & DEG;. An additional advantage of the proposed method is the ability to predict the location of the sound source from a single signal analysis frame. This gives instant localization and is in line with many alternative applications. The proposed solution does not require intensive preprocessing of the audio signals and can be used as a video camera pointing system based on a microphone array. In the future, it would be relevant to investigate the localization performance of more than two sound sources, and the variable acoustic conditions could also be assessed.

关键词： Location awareness Microphone arrays Acoustics clustering algorithms Direction-of-arrival estimation Cameras Estimation clustering convolutional neural networks microphone array multiple sound sources sound source localization three-dimensional

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Adaptive Multifrequency Attribute Analysis and Its Application on Reservoir Characterization

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 18-18页

作者： Zhang, Zezhou Liu, Naihao Liu, Rongchang Lu, Man Wei, Tao Gao, Jinghuai Xi An Jiao Tong Univ Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China CNPC PetroChina Res Inst Petr Explorat & Dev RIPED Beijing 100083 Peoples R China China Univ Petr Sch Geosci Beijing 102249 Peoples R China CNPC Yumen Oilfield Co Res Inst Explorat & Dev Jiuquan 735019 Gansu Peoples R China

Seismic frequency attributes are commonly used for describing geological structures and characterizing complex reservoirs. Although there are different kinds of machine learning (ML)-based methods proposed, how to select appropriate attributes and how to map them to reservoir thickness is still an open issue. In this study, we suggest an adaptive multifrequency attribute analysis (AMFAA)-based workflow to address these issues. We first utilize a generalized S-transform to extract multifrequency attributes, which can describe local time-frequency features of seismic data. Then, we propose a sensitive attribute analysis (SAA) method to reduce frequency attribute redundancy with the aid of hierarchical clustering and correlation analysis. Afterward, based on the selected sensitive attributes, we propose to adopt the potential of heat diffusion for affinity-based transition embedding (PHATE) for multifrequency attribute analysis, which can map seismic multifrequency attributes to reservoir thickness. To test the validity and effectiveness of our method, we first apply it to a synthetic wedge model and then post-stack field data in the Ordos Basin, Northwest China.

关键词： Reservoirs Geology clustering algorithms Heating systems Time-frequency analysis Data mining Three-dimensional displays Machine learning (ML) multifrequency attribute reservoir characterization seismic attribute

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CNN-Based Object Detection via Segmentation Capabilities in Outdoor Natural Scenes

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IEEE ACCESS 2024年 12卷 84984-85000页

作者： Naseer, Aysha Al Mudawi, Naif Abdelhaq, Maha Alonazi, Mohammed Alazeb, Abdulwahab Algarni, Asaad Jalal, Ahmad Air Univ Dept Comp Sci Islamabad 44000 Pakistan Najran Univ Coll Comp Sci & Informat Syst Dept Comp Sci Najran 55461 Saudi Arabia Princess Nourah Bint AbdulRahman Univ Coll Comp & Informat Sci Dept Informat Technol POB 84428 Riyadh 11671 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Dept Informat Syst Al Kharj 16273 Saudi Arabia Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Ar Ar 91911 Saudi Arabia

Object recognition along with classification are necessary for many applications, such as surveillance systems, car plate recognition, traffic monitoring, and face detection. Unlike existing approaches, ours incorporates a wide range of important factors to improve recognition precision. The primary phase in the image accumulating process is preprocessing, when semantic segmentation proves its usefulness by accurately defining the physical borders of specific objects inside an image in addition to recognizing them. This paper presents a novel approach to accurate object recognition. Segmentation incorporates previously identified homologous and related groups after employing the K-means clustering technique to group analogous colors and spatial patterns. Convolutional Neural Network (CNN) technology is ultimately used to identify objects in different environmental circumstances. Performance metrics like as F1 Score=0.948, Precision = 0.968, and Recall=0.932 for MSRC and F1 Score=0.921, Precision = 0.951, and Recall=0.891 for Caltech 101 and F1 Score=0.847, Precision = 0.879, and Recall=0.827 over Pascal Voc 2012 demonstrate the efficiency of our strategy. The efficacy of the suggested method is evaluated using multiple benchmark datasets, MSRC-v2, Caltech 101 and Pascal Voc 2012, yielding recognition accuracies of 92.25%, 91.91% and 93.50% respectively, when tested against the Microsoft Research Cambridge (MSRC), California Institute of Technology 101 Object Categories (Caltech 101) and Pascal Voc 2012 datasets.

关键词： Object recognition Image segmentation Object detection Feature extraction Image recognition Convolutional neural networks Accuracy clustering algorithms Machine learning clustering machine learning segmentation feature fusion object recognition convolutional neural network

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Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

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IEEE ACCESS 2024年 12卷 50310-50320页

作者： Ma, Chao Wang, Juan Zeng, Tiwei Liang, Qifu Lan, Xinguo Lin, Shaoming Fu, Wei Liang, Lvsheng Hainan Univ Coll Mech & Elect Engn Haikou 570228 Peoples R China Hainan Univ Coll Informat & Commun Engn Haikou 570228 Peoples R China Hainan Zhongnong Hangfu Technol Co Ltd Danzhou Peoples R China

Banana phenotypic parameters are one of the important elements in the study of banana growth and development. Through the measurement of banana phenotypic parameters, we can obtain information about the growth status, nutritional status and quality indexes of banana plants, and pseudo-stem parameters are significant indicators in banana phenotypic parameters. This research proposes a two-stage approach combining morphological features and deep learning point cloud segmentation to extract banana pseudo-stem parameters. Specifically, in the first step, seed points are extracted using the DBSCAN clustering algorithm, and banana individual plant segmentation is accomplished using the region growing algorithm based on seed points. Its precision, recall and F1-score were 97.73%, 97.36% and 97.54%, respectively. This indicates that the DBSCAN clustering algorithm and the seed point based region growing algorithm can effectively realize the plant count of banana plants and initially realize the individual plant segmentation of banana. The second step is to use PointNet++, PointNet, and DGCNN for pseudo-stem and canopy segmentation of individual banana plants. All three models perform well in segmentation, with PointNet++ performing the best. Its precision, recall, F1-score, Matthews correlation coefficient and Dice coefficient reached 0.9956, 0.9709, 0.9831, 0.9670 and 0.9831. This shows that deep learning has a better applicability in segmenting banana plants. In the results of segmentation, we measure the banana pseudo-stem circumference and pseudo-stem height. The correlation between the extracted pseudo-stem height and pseudo-stem circumference compared to the measured values was 96.70% and 82.32%, respectively. The above two-stage method of extracting banana pseudo-stem parameters overcomes the difficulties of point cloud individual plant segmentation associated with intensive banana cultivation. It makes the management of individual banana plants possible and provides

关键词： Point cloud compression clustering algorithms Plants (biology) Seeds (agriculture) Deep learning Sea measurements Prediction algorithms Laser radar Phenology Parameter estimation Terrestrial LiDAR individual segmentation phenotypic parameter deep learning

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Robust Cooperative Tracking for Aerial Maneuvering Target With Faulty Sensors

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2024年第3期60卷 2894-2908页

作者： Zhang, Zheng Dong, Xiwang Zhang, Yvjie Yu, Jianglong Ren, Zhang Beihang Univ Sch Automat Sci & Elect Engn Sci & Technol Aircraft Control Lab Beijing 100191 Peoples R China Beihang Univ Inst Artificial Intelligence Beijing 100191 Peoples R China

This article considers the robust cooperative aerial maneuvering target tracking problem based on the wireless sensor network with faulty sensors. The proposed robust K-means distributed cubature information filter (K-DCIF) algorithm is designed by three stages, namely, local filter, cluster, and consensus fusion. Each sensor has processing ability, which can be used to complete the local filtering stage individually. During the clustering stage, the K-means method is introduced to divide all the sensors in the sensor network into faulty sensors and reliable sensors. Then, the information matrix and the information vector obtained from the reliable sensors constitute information pairs during the consensus fusion stage. Based on the local neighboring interactions in the network, the accurate state information, such as position, velocity, and acceleration of the aerial maneuvering target, can be obtained by each sensor. Furthermore, by introducing a stochastic process, the boundedness of the estimation error of the K-DCIF algorithm with faulty sensors is proved. Finally, numerical simulation and equivalent experiment for maneuvering target tacking are given to validate the performance of the proposed algorithm.

关键词： Target tracking Estimation clustering algorithms Robot sensing systems Wireless sensor networks State estimation Mathematical models Aerial maneuvering target equivalent experiment faulty sensors wireless sensor network

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A Modular Framework for Centrality and clustering in Complex Networks

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IEEE ACCESS 2022年 10卷 40001-40026页

作者： Oggier, Frederique Phetsouvanh, Silivanxay Datta, Anwitaman Nanyang Technol Univ Div Math Sci Singapore 639798 Singapore Minist Technol & Commun Viangchan Laos Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

The structure of many complex networks includes edge directionality and weights on top of their topology. Network analysis that can seamlessly consider combination of these properties are desirable. In this paper, we study two important such network analysis techniques, namely, centrality and clustering. An information-flow based model is adopted for clustering, which itself builds upon an information theoretic measure for computing centrality. Our principal contributions include (1) a generalized model of Markov entropic centrality with the flexibility to tune the importance of node degrees, edge weights and directions, with a closed-form asymptotic analysis, which (2) leads to a novel two-stage graph clustering algorithm. The centrality analysis helps reason about the suitability of our approach to cluster a given graph, and determine 'query' nodes, around which to explore local community structures, leading to an agglomerative clustering mechanism. Our clustering algorithm naturally inherits the flexibility to accommodate edge directionality, as well as different interpretations and interplay between edge weights and node degrees. Extensive benchmarking experiments are provided, using both real-world networks with ground truth and synthetic networks.

关键词： clustering algorithms Markov processes Weight measurement Computational modeling Bitcoin Analytical models Absorption Directed weighted graphs entropy centrality graph clustering random walkers

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Brain EEG Time-Series clustering Using Maximum-Weight Clique

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第1期52卷 357-371页

作者： Dai, Chenglong Wu, Jia Pi, Dechang Becker, Stefanie, I Cui, Lin Zhang, Qin Johnson, Blake Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing 211106 Peoples R China Macquarie Univ Dept Comp Sydney NSW 2109 Australia Univ Queensland Sch Psychol St Lucia Qld 4072 Australia Suzhou Univ Intelligent Informat Proc Lab Suzhou 234000 Peoples R China Univ Technol Sydney Ctr Artificial Intelligence Ultimo NSW 2007 Australia Tencent Data Ctr Social Network Grp Shenzhen 518057 Peoples R China Macquarie Univ Dept Cognit Sci Sydney NSW 2109 Australia

Brain electroencephalography (EEG), the complex, weak, multivariate, nonlinear, and nonstationary time series, has been recently widely applied in neurocognitive disorder diagnoses and brain-machine interface developments. With its specific features, unlabeled EEG is not well addressed by conventional unsupervised time-series learning methods. In this article, we handle the problem of unlabeled EEG time-series clustering and propose a novel EEG clustering algorithm, that we call mwcEEGc. The idea is to map the EEG clustering to the maximum-weight clique (MWC) searching in an improved Frechet similarity-weighted EEG graph. The mwcEEGc considers the weights of both vertices and edges in the constructed EEG graph and clusters EEG based on their similarity weights instead of calculating the cluster centroids. To the best of our knowledge, it is the first attempt to cluster unlabeled EEG trials using MWC searching. The mwcEEGc achieves high-quality clusters with respect to intracluster compactness as well as intercluster scatter. We demonstrate the superiority of mwcEEGc over ten state-of-the-art unsupervised learning/clustering approaches by conducting detailed experimentations with the standard clustering validity criteria on 14 real-world brain EEG datasets. We also present that mwcEEGc satisfies the theoretical properties of clustering, such as richness, consistency, and order independence.

关键词： Electroencephalography Time series analysis Electrodes Feature extraction Correlation clustering algorithms Australia clustering electroencephalography (EEG) time series Frechet distance (FD) maximum-weight clique (MWC) weighted EEG graph

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Comparative Study of Physical Education Teaching in Middle Schools at Home and Abroad Using clustering Algorithm

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COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022年第0期2022卷 7277742页

作者： Tan, Dejun Huizhou Univ Huizhou 516001 Peoples R China

Physical education in middle school is very important for teenagers, so it is also crucial to understand the differences between PET (Physical Education Teaching) systems in middle schools at home and abroad. The frontier and hotspot of PET research in middle schools at home and abroad are examined in this paper using citation analysis, information visualization, and cluster analysis, as well as CiteSpace software. The findings show that PET method research in China is qualitative, whereas PET method research in middle schools around the world is quantitative evaluation and empirical research. Domestic research hotspots focus on classroom instructional design, whereas foreign countries focus on load identification theory's application in instructional design. Frontier research in the United States is dispersed and covers a wide range of topics, whereas research in other countries focuses on cognitive load theory. The classification time of this improved algorithm is reduced by 190.97 seconds when compared to the traditional KNN algorithm, and the total time is increased by more than 50%. According to the findings, nonsports or nonsports influencing factors should be given more consideration in the study of adolescent physical fitness decline in China.

关键词： clustering algorithms

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