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Identification of Damaged Building Regions From High- Resolution Images Using Superpixel-Based Gradient and Autocorrelation Analysis

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 1010-1024页

作者： Liu, Chaoxian Sui, Haigang Huang, Lihong Wuhan Univ State Key Lab Informat Engn Surveying Mappint & R Wuhan 430079 Peoples R China

An efficient and accurate identification of damaged buildings after earthquakes is critical for ensuring a quick response and efficient rescue operations. Considering the time pressure during disaster emergency response and the convenience of practical application, a new approach for identifying areas with damaged buildings is proposed in this study. The approach is validated on high-resolution images from areas impacted by the Haiti earthquake. The approach consists of the following steps. (i), the simple linear iterative clustering method is improved to segment the images into high-quality superpixels by combining both color and texture information;(ii) the characteristics of the damage in the area within the cluster and the difference between pre- and post-earthquake images are obtained at superpixel level using the Sobel gradient;and (iii) local indicators from the spatial association analysis are used to extract the result of the gradient clustering using the synthetic- damage index calculated by an improved relief algorithm;and (iv) vegetation and shadows are masked out to identify the damaged buildings. This framework achieved an overall accuracy of similar to 90% in terms of visual interpretation, with precision and recall of similar to 85% and similar to 90%, respectively. Inaccuracies mainly occur around the boundaries of the damaged building regions. It is evaluated that the proposed framework is more convenient compared to other supervised and unsupervised methods. The successful application in different scenes demonstrates that the proposed framework has the ability to rapidly and accurately identify regions with damaged buildings, which is beneficial for post-disaster emergency assessment.

关键词： Buildings Feature extraction Indexes Image color analysis clustering algorithms Image segmentation Earthquakes Earthquake building damage simple linear Iterative clustering synthetic damage index improved-relief local indicators of spatial association analysis

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A Secure Data Gathering Scheme Based on Properties of Primes and Compressive Sensing for IoT-Based WSNs

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IEEE SENSORS JOURNAL 2021年第4期21卷 5553-5571页

作者： Salim, Ahmed Osamy, Walid Khedr, Ahmed M. Aziz, Ahmed Abdel-Mageed, Mohamed Qassim Univ Dept Comp Sci Coll Sci & Arts Buraydah 51931 Saudi Arabia Zagazig Univ Fac Sci Zagazig 44519 Egypt Qassim Univ Dept Appl Nat Sci Unaizah Coll Community Unaizah 51911 Saudi Arabia Benha Univ Fac Comp & Artificial Intelligence Comp Sci Dept Banha 13511 Egypt Univ Sharjah Comp Sci Dept Sharjah 27272 U Arab Emirates Tashkent State Univ Econ Dept Int Business Management Tashkent 100066 Uzbekistan Qassim Univ Unaizah Coll Sci & Arts Dept Phys Unaizah 51911 Saudi Arabia

Minimizing the quantity of transmitted data and protecting the devices from the adversary environment are the main challenges hindering the advancement of Internet of Things (IoT) network. Based on the theorem of unique prime factorization that any natural number can be expressed as the product of powers of prime numbers, we introduce a new scheme in this paper for secure and efficient gathering of the IoT data. The proposed scheme consists of three phases: Setup, Secure data gathering, and Data reconstruction. In the Setup phase, the properties of the prime numbers to create an efficient clustering and routing tree algorithm are utilized to reduce the power consumption during data transmission process. The Secure data gathering phase includes secure intra and inter communication procedures. During secure intra-communication procedure, the Compressive Sensing (CS) based method of data compression and encryption is used. Moreover, the prime number properties and the Bezier curve function are used to solve the CS problems. In the secure inter-communication procedure, the security performance of the IoT based Wireless Sensor Networks (WSNs) is improved by combining Private and Public key algorithms with the consideration of the power limitation of IoT devices. Finally, the proposed scheme introduces an adept reconstruction algorithm which is a hybrid algorithm of Bees and Genetic Algorithm to successfully restore the actual sensor data from the compressed samples, which improves the reconstruction process.

关键词： Wireless sensor networks Encryption Sensors clustering algorithms Compressed sensing Routing Internet of Things compressive sensing secure data gathering wireless sensor network properties of primes

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Secondary Crash Identification using Crowdsourced Waze User Reports

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TRANSPORTATION RESEARCH RECORD 2021年第10期2675卷 853-862页

作者： Zhang, Zhihua Liu, Yuandong Han, Lee D. Freeze, Phillip Bradley Univ Tennessee Knoxville TN 37996 USA Tennessee Dept Transportat Nashville TN USA

Secondary crashes are crashes that occur as a result of the nonrecurrent congestion originating from primary crashes, and always have a greater impact on safety and traffic than a single crash. A better understanding of secondary crashes would benefit traffic incident management, and this requires accurate identification of secondary crashes. This study explores using crowdsourced Waze user reports to identify secondary crashes. A network-based clustering algorithm is proposed to extract the primary crash cluster, including all user reports originating from the primary crash, and any crash that occurred within the cluster would be a secondary crash. This method works as a filter to select accurate primary-secondary relationships, thus precisely identifying secondary crashes. A case study is performed with crashes occurring from June to December 2019 on a 30-mi stretch of I-40 in Knoxville, TN. A static threshold method (crash duration and 10 mi) was used to preselect the potential primary-secondary crash pairs, and 75 out of 708 crashes were identified as potential secondary crashes. Based on the preselected primary-secondary crash pairs, 17 secondary crashes were obtained with the proposed method and the results were compared with one of the commonly used methods, the speed contour plot method. Though the proposed method captured fewer secondary crashes, it did identify several secondary crashes that could not be observed with the speed contour plot method. The results showed the applicability of the method and the potential of crowdsourced Waze user reports in secondary crash identification.

关键词： clustering algorithms

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Hybrid Learning Algorithm of Radial Basis Function Networks for Reliability Analysis

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IEEE TRANSACTIONS ON RELIABILITY 2021年第3期70卷 887-900页

作者： Zhang, Dequan Zhang, Ning Ye, Nan Fang, Jianguang Han, Xu Hebei Univ Technol Sch Mech Engn State Key Lab Reliabil & Intelligence Elect Equip Tianjin 300401 Peoples R China Univ Technol Sydney Sch Civil & Environm Engn Sydney NSW 2007 Australia

With the wide application of industrial robots in the field of precision machining, reliability analysis of positioning accuracy becomes increasingly important for industrial robots. Since the industrial robot is a complex nonlinear system, the traditional approximate reliability methods often produce unreliable results in analyzing its positioning accuracy. In order to study the positioning accuracy reliability of industrial robot more efficiently and accurately, a radial basis function network is used to construct the mapping relationship between the uncertain parameters and the position coordinates of the end-effector. Combining with the Monte Carlo simulation method, the positioning accuracy reliability is then evaluated. A novel hybrid learning algorithm for training radial basis function network, which integrates the clustering learning algorithm and the orthogonal least squares learning algorithm, is proposed in this article. Examples are presented to illustrate the high proficiency and reliability of the proposed method.

关键词： Reliability Service robots Robot kinematics Power system reliability Radial basis function networks clustering algorithms Hybrid learning algorithm (HLA) industrial robot positioning accuracy radial basis function network (RBFN) reliability analysis

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Deep Generative Models to Counter Class Imbalance: A Model-Metric Mapping With Proportion Calibration Methodology

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IEEE ACCESS 2021年 9卷 55879-55897页

作者： Mirza, Behroz Haroon, Danish Khan, Behraj Padhani, Ali Syed, Tahir Q. Natl Univ Comp & Emerging Sci Sch Comp Karachi 75030 Pakistan Inst Business Adm Karachi 75270 Pakistan

The most pervasive segment of techniques in managing class imbalance in machine learning are re-sampling-based methods. The emergence of deep generative models for augmenting the size of the under-represented class, prompts one to review the question of the suitability of the model chosen for data augmentation with the metric selected for the-goodness-of classification. This work defines this suitability by using newly-sampled data points from each generative model first to the degree of parity, and studying classification performance on a large set of metrics. We extend the investigation to different proportions of augmented data points for identifying the sensitivity of the metric to the degree of imbalance, leading to the discovery of an optimum proportion against the metric. The models used are GAN, VAE and RBM and the metrics include Precision, Recall, F1-Score, AUC, G-Mean and Balanced Accuracy. We offer a comparison of these models with the established class of data synthesizing counterparts on the aforementioned metrics. Deep generative models outperform the state-of-the-art on 5 metrics on multiple datasets and also comprehensively surpass the baselines. This work thereby recommends the following model-metric mappings: VAE for high Precision and F1-Score, RBM for high Recall and GAN for high AUC, G-Mean and Balanced Accuracy under various recommended proportions of the minority class.

关键词： Measurement Data models Mathematical model Context modeling clustering algorithms Sensitivity Manifolds Adversarial networks anomaly detection class imbalance deep generative models density estimation generative variational auto encoders instance hardness threshold machine learning best practices restricted Boltzmann machines

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Rational Inattention in Choice Overload: clustering for Discrete Choices 55

Rational Inattention in Choice Overload: Clustering for Disc...

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55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021

作者： Sharma, Pankaj Varshney, Lav R. University of Illinois Coordinated Science Laboratory Urbana-Champaign United States

ISBN: (纸本)9781665458283

Discrete choice models in economics are often used to mathematically model the heuristic approaches that people use in decision-making. When faced with a large number of choices, however, people face information costs that lead to choice overload. Within the discrete choice framework, here we formulate a quantization-theoretic approach to optimally cluster choices into categories. This is a non-asymptotic form of rational inattention theory. Drawing on a recent equivalence result between discrete choice models and Bregman divergences, and on properties of Bregman clustering, our main result is that the same clustering algorithm is universally optimal for any additive random utility discrete choice model. Examples are given and hierarchical clustering is also discussed. © 2021 IEEE.

关键词： clustering algorithms

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Hybridization of Metaheuristic Algorithm for Dynamic Cluster-Based Routing Protocol in Wireless Sensor Networksx

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IEEE ACCESS 2021年 9卷 83751-83761页

作者： Al-Otaibi, Shaha Al-Rasheed, Amal Mansour, Romany F. Yang, Eunmok Joshi, Gyanendra Prasad Cho, Woong Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Informat Syst Dept Riyadh 11564 Saudi Arabia New Valley Univ Fac Sci Dept Math El Kharga 72511 Egypt Kookmin Univ Dept Financial Informat Secur Seoul 02707 South Korea Sejong Univ Dept Comp Sci & Engn Seoul 05006 South Korea Daegu Catholic Univ Dept Automot ICT Convergence Engn Gyongsan 38430 South Korea

Energy efficiency is considered the major design issue in wireless sensor networks (WSN), which can be addressed using clustering and routing techniques. They are treated as Non-deterministic Polynomial (NP)-hard optimization problems and are solved using metaheuristic algorithms to identify the optimal or near-optimal solutions. With this motivation, this paper develops a hybridization of the metaheuristic cluster-based routing (HMBCR) technique for WSN. The HMBCR technique initially involves a brainstorm optimization with levy distribution (BSO-LD) based clustering process using a fitness function incorporating four parameters such as energy, distance to neighbors, distance to the base station, and network load. Besides, a water wave optimization with a hill-climbing (WWO-HC) based routing process is carried out for optimal route selection. Extensive experimentation analysis is performed to ensure the energy efficiency and network lifetime performance of the HMBCR technique. The experimental outcome ensured the superior results of the HMBCR technique over the compared methods under different aspects.

关键词： Wireless sensor networks Routing Energy efficiency Routing protocols Optimization clustering algorithms Voting clustering energy efficiency metaheuristics routing WSN

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A Novel Time-Aware Hybrid Recommendation Scheme Combining User Feedback and Collaborative Filtering

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IEEE SYSTEMS JOURNAL 2021年第4期15卷 5301-5312页

作者： Li, Hongzhi Han, Dezhi Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China

Nowadays, recommender systems are used widely in various fields to solve the problem of information overload. Collaborative filtering and content-based are representative solutions in recommender systems, however, the content-based model has some shortcomings, such as single kind of recommendation results, lack of effective perception of user preferences;while, for the collaborative filtering model, there is a cold start problem, and such a model is greatly affected by its adopted clustering algorithm. To address these issues, a hybrid recommendation scheme is proposed in this article, which is based on both collaborative filtering and content-based. In this scheme, we propose the concept of time impact factor, and a time-aware user preference model is built based on it. Also, user feedback on recommendation items is utilized to improve the accuracy of our proposed recommendation model. Finally, the proposed hybrid model combines the results of content recommendation and collaborative filtering based on the logistic regression algorithm.

关键词： Computational modeling Recommender systems Collaboration Internet Time factors Sparse matrices clustering algorithms Collaborative filtering (CF) time-aware time impact factor user feedback

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A Community Detection Method for Social Network Based on Community Embedding

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第2期8卷 308-318页

作者： Li, Meizi Lu, Shuyi Zhang, Lele Zhang, Yuping Zhang, Bo Shanghai Normal Univ Coll Informat Mech & Elect Engn Shanghai 200234 Peoples R China Shanghai Normal Univ Inst Artificial Intelligence Educ Shanghai 200234 Peoples R China Shanghai Normal Univ Shanghai Engn Res Ctr Intelligent Educ & Bigdata Shanghai 200234 Peoples R China

Most community detection methods focus on the similarities between detection nodes to achieve community partitioning. Traditional network representation learning methods are also limited to the local context of the central nodes, which results in less truly representative results. This article examines nodes' influence information, nodes' community affiliating information, and similarity of community topologies and proposes a more effective node representation strategy. According to the local node information and global topology in the social network graph, a method of combining local node embedding and global community embedding is also designed. The effectiveness of learning node representation and community representation is improved by our approach. The proposed model can also effectively detect overlapping communities.

关键词： clustering algorithms Social networking (online) Topology Network topology Machine learning algorithms Natural language processing Training Community embedding node belonging community information node embedding node influence information overlapping community detection

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A Study on the Characteristics of College Students’ Consumption Behavior Based on clustering and Association Rules 7th

A Study on the Characteristics of College Students’ Consump...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Wang, Jie Chen, XiWen Cheng, KaiRui Cao, YanLi Pan, Bin Hunan University of Finance and Economics Changsha410205 China

ISBN: (纸本)9783030786175

In 21st century, the development of information technology has led to an overall improvement in people’s material level. Especially the big consumer group of college students. Campus information technology has now become a trend, which means that there is a large amount of consumer data accumulation. Behind the huge amount of behavioral data, there is potentially valuable information. By analyzing the data, we can understand the characteristics and needs of the consumption behavior of college students, so that we can better promote the process of campus informationization and help campus management. © 2021, Springer Nature Switzerland AG.

关键词： clustering algorithms

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