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作者： Qizhang Shen Mechanical Engineering Department University of California

In the past decades,Collision Avoidance System has grown to a level more developed than it has ever been,actively and significantly improving the safety of passengers of ***,it only focused on the period of time when the vehicles are driving,and ignored the moment when passengers are most vulnerable – when they are getting off the car as their attention to the surrounding drops and are no longer protected by the structure of the ***,in this paper,an additional system is proposed to warn the passengers to open the doors and get off with caution,and even interfere to stop them getting off temporarily.

关键词： automobile engineering automotive safety collision avoidance door crashes laser radar clustering algorithm

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Supporting decision makers in search-based product line architecture design using clustering 43

Supporting decision makers in search-based product line arch...

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43rd IEEE-Computer-Society Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Freire, Willian Marques Bindewald, Carlos Vinicius Amaral, Aline M. M. Miotto Colanzi, Thelma Elita Univ Estadual Maringa Informat Dept State Univ Maringa UEM Maringa Parana Brazil

ISBN: (纸本)9781728126074

The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line (SPL). PLA design can be formulated as an optimization problem with many factors. In this context, the MOA4PLA approach was proposed to optimize PLA design using search algorithms and metrics specific to the context. MOA4PLA treats the PLA design as a multi-objective optimization problem. At the end of the search process several PLA design alternatives are presented to the decision maker, difficulting the decision about which PLA alternative best fits with his/her needs. In this sense, this work proposes the usage of clustering algorithms to group PLAs design alternatives, according to their characteristics and assist the decision maker in the choice of one PLA design for the SPL. For this purpose, an empirical study was carried out, involving quantitative and qualitative experiments. Such an study integrated the K-Means++ and DBSCAN clustering algorithms in the MOA4PLA approach. The results of the experiments were promising, since an appropriate grouping of the solutions can be quantitatively observed, and also, qualitatively, the suitability of the solutions to the decision makers needs was verified.

关键词： product line architecture multi-objective optimization clustering algorithm K-Means plus DBSCAN

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Solving customer insurance coverage recommendation problem using a two-stage clustering-classification model

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INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE AND ENGINEERING MANAGEMENT 2019年第1期14卷 9-19页

作者： Khalili-Damghani, Kaveh Abdi, Farshid Abolmakarem, Shaghayegh Islamic Azad Univ South Tehran Branch Dept Ind Engn Tehran Iran

Insurance coverage recommendation problem (ICRP) in which the most suitable coverage for customers is suggested is an essential issue for an insurance company. ICRP helps insurance companies to give suitable services to their customers. In ICRP, the insurance company tried to mine the features and records of data associated with the customers to suggest them the most economic and fitted insurance plan. The insurance companies have large databases which are considered as a proper infrastructure to analyze, model and predict the customer behavior. In this paper, a two-stage clustering-classification model is proposed to suggest suitable insurance coverage for customers. The first stage addresses a data pre-screening phase and clustering of customers based on the record of insurance coverage. Well-known clustering algorithms are employed. The superior clustering algorithm is selected based on Davies-Bouldin metric. In the second stage, several filter and wrapper methods are implemented to select proper features. The selected features are assumed as inputs of K-nearest neighbor classification algorithm. The proposed approach is applied in a real case study for clustering the customers and recommend insurance coverage. The results show that the model is capable of suggesting suitable insurance coverage based on customers' characteristics. [GRAPHICS] .

关键词： Knowledge engineering consumer behavior modeling data mining clustering algorithm classification algorithm insurance coverage recommendation

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Bio-inspired energy efficient clustering approach for wireless sensor networks

Bio-inspired energy efficient clustering approach for wirele...

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International Conference on Wireless Networks and Mobile Communications (WINCOM)

作者： Agbehadji, Israel Edem Millham, Richard C. Fong, Simon James Jung, Jason J. Bui, Khac-Hoai Nam Abayomi, Abdultaofeek Frimpong, Samuel Ofori Durban Univ Technol Dept Informat Technol ICT & Soc Res Grp Durban South Africa Univ Macau Dept Comp & Informat Sci ICT & Soc Res Grp Taipa Macau Peoples R China Chung Ang Univ Seoul South Korea Korea Inst Sci & Technol Informat Daejeon South Korea

ISBN: (纸本)9781728126258

In this paper, we proposed an approach to clustering based on bio-inspired behaviour and distributed energy efficient model. The motivation to propose this clustering approach is due to the challenge of performance in terms of finding an efficient way to send data packets to base stations and to maintain the lifetime performance of wireless sensor networks. The bio-inspired approach adopted the behaviour of a bird called Kestrel. This behaviour is expressed using mathematical formulation and then translated into an algorithm. The bio-inspired algorithm is combined with the distributed energy efficient model for clustering to ensure efficient energy optimization. The proposed clustering approach, referred to as DEEC-KSA, is evaluated through simulation and compared with benchmarked clustering algorithms. The result of simulation showed that the performance of DEEC-KSA is efficient among the comparative clustering algorithms for energy optimization in terms of stability period, network lifetime and network throughput. Additionally, the proposed DEEC-KSA has the optimal time (in seconds) to send packets to base station successfully.

关键词： Edge computing load balancing wireless sensor network clustering algorithm kestrel-based search algorithm heterogeneous environment internet of things (IoT) analytics

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Research on Scientific Bibliography clustering Technology Based on Citation Information Merging 2

Research on Scientific Bibliography Clustering Technology Ba...

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2nd International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yin, Baosheng Song, Zhiwei Yan, Yusheng Sun, Mengyang Shenyang Aerosp Univ Res Ctr Human Comp Intelligence Shenyang 110136 Peoples R China China Inst Marine Technol & Econ Beijing 100081 Peoples R China

ISBN: (纸本)9781728108315

clustering analysis is an important step in extracting and analyzing technology bibliography trend. The bibliographic records, consisting of the title, authors, keywords, and publications, are short texts carrying less information than long texts, while bibliographic network has the citation relations among bibliographies. The experimental dataset therein refers to bibliographies in the computer field crawled from the existing literature databases. Based on traditional Hierarchical clustering algorithm, this paper merges the cocitation relations. The experiment result shows that the mean silhouette coefficient of the Agglomerative clustering algorithm that merges the co-citation relations among the bibliographies is improved obviously, thus effectively improving clustering result among bibliographies with strong co-citation relations, and good clusters provide a solid research basis for the follow-up bibliography trend analysis.

关键词： natural language processing clustering algorithm scientific bibliography citation relations

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Trust-Aware clustering Approach Using Type-2 Fuzzy Logic for Mobile Wireless Sensor Networks 3rd

Trust-Aware Clustering Approach Using Type-2 Fuzzy Logic for...

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3rd International Conference on Advanced Informatics for Computing Research (ICAICR)

作者： Kousar, Asra Mittal, Nitin Singh, Prabhjot Chandigarh Univ Elect & Commun Engn Mohali 140413 Punjab India

ISBN: (纸本)9789811501111;9789811501104

Mobile Wireless Sensor Networks (MWSNs) are a definitive area for applications such as basic security activities, military reconnaissance and observing forest fire. In MWSNs appropriate selection of cluster head (CH) is to enhance the network lifetime. On taking the security of network into account is a challenging task. Trustworthy data collection is a major topic that interests much research work. Trust plays an important role in military and other applications. Many algorithms do not take security into account while selecting CH for MWSNs. Existing security-aware protocols use the cryptographic method, which are not enough to overcome serious issues. The cryptographic technique causes complexity in the network, a large amount of overhead and poor connectivity. Therefore, this paper proposes a trust-aware approach for MWSNs using type-2 fuzzy logic (T2FL). Trust value is considered as a major parameter that affects the performance of nodes. In this approach, to elect secure CH, trust value, remaining battery power, concentration, distance to base station and moving speed is used. Experimental analysis shows that this approach can successfully eliminate the malicious node in MWSN.

关键词： WSN clustering algorithm Cluster head Type-1 fuzzy logic Type-2 fuzzy logic Mobility Sensor network

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Enhanced Urban clustering in VANETs Using Online Machine Learning

Enhanced Urban Clustering in VANETs Using Online Machine Lea...

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IEEE International Conference on Vehicular Electronics and Safety (ICVES)

作者： Alsuhli, Ghada H. Khattab, Ahmed Fahmy, Yasmine A. Massoud, Yehia Cairo Univ Elect & Elect Commun Engn Dept Giza Egypt Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

ISBN: (纸本)9781728134734

clustering in Vehicular Ad-Hoc Networks (VANETs) is essential to mitigate different challenges and meet the required quality of communications. However, most of the available clustering protocols were designed for highways, and thus become unstable in realistic urban environments with many intersections. In this paper, a clustering Adaptation Near Intersection (CANI) approach is proposed to ensure clustering stability at intersections. This approach exploits Online Sequential Extreme Learning Machine (OS-ELM) to predict the behavior of the vehicles near an intersection and adapt the clusters accordingly. The main advantage of the developed OS-ELM prediction model is its ability to continuously learn and update in real time. After being validated, the proposed adaptation approach is included in a highway clustering scheme. The resultant clustering protocol is compared to other schemes in a realistic urban environment, and shows significant stability and efficiency performance improvement.

关键词： VANET clustering algorithm urban environment machine learning

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Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第8期67卷 2115-2128页

作者： Bu, Yuheng Zou, Shaofeng Veeravalli, Venugopal V. Univ Illinois ECE Dept Urbana IL 61801 USA Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA Univ Buffalo State Univ New York Dept Elect Engn Buffalo NY 14228 USA

The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among M sequences of samples. A sequence is considered as outlying if the observations therein are generated by a distribution different from those generating the observations in the majority of the sequences. In the universal setting, we are interested in identifying all the outlying sequences without knowing the underlying generating distributions. We consider the outlying sequence detection problem in three different scenarios: first, known number of outlying sequences;second, unknown number of identical outlying sequences;and finally, typical and outlying distributions forming clusters. In this paper, a class of tests based on distribution clustering is proposed. These tests are shown to be exponentially consistent with linear time complexity in M. Numerical results demonstrate that our clustering-based tests achieve similar performance to existing tests, while being considerably more computationally efficient.

关键词： Anomaly detection clustering algorithm exponential consistency outlier detection universal outlier hypothesis testing

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Implementation of machine learning techniques into the Subset Simulation method

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STRUCTURAL SAFETY 2019年 79卷 12-25页

作者： Cui, Fengkun Ghosn, Michel Shandong Jiaotong Univ Civil Engn Jinan 250357 Shandong Peoples R China CUNY City Coll Civil Engn New York NY 10031 USA

A hybrid reliability analysis procedure that combines the advantages of the Subset Simulation method, the application of surrogate models and clustering techniques is proposed to efficiently assess the failure probability of complex structural systems that may exhibit multiple failure modes. The proposed procedure, herein called SS-KK, integrates the Kriging surrogate modeling technique, K-means clustering algorithm into the original Subset Simulation (SS) method. The approach is found to not only improve the efficiency of the Subset Simulation method in finding the reliability of a structural system but to also help identify the important failure modes and controlling random variables. This information is important to aid engineers optimize the structural design process by focusing on the critical failures modes and design variables. The method consists of first constructing a relatively coarse global Kriging model at each subset level of SS by applying an appropriate active learning strategy. Subsequently, the initial global Kriging model is partitioned into several local Kriging models that coincide with the important failure modes identified by the K-means clustering algorithm. This partitioning, which helps identify the important failure modes, also gives a better representation of the entire failure region leading to improved estimates of the system reliability. Finally, FORM is implemented for each local Kriging to rank the importance of each identified failure mode based on its Hasofer-Lind reliability index and to use the associated design point and sensitivity coefficients to identify the most critical random variables. Several examples extracted from the available literature are analyzed to illustrate the advantages of the proposed SS-KK methodology and demonstrate its accuracy and efficiency.

关键词： Structural system reliability Subset Simulation Kriging model clustering algorithm Multiple failure modes

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Deployment Strategies of Multiple Aerial BSs for User Coverage and Power Efficiency Maximization

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IEEE TRANSACTIONS ON COMMUNICATIONS 2019年第4期67卷 2981-2994页

作者： Sun, Jingcong Masouros, Christos UCL Dept Elect & Elect Engn London WC1E 7JE England

Unmanned aerial vehicle-based aerial base stations (BSs) can provide rapid communication services to ground users and are thus promising for future communication systems. In this paper, we consider a scenario where no functional terrestrial BSs are available and the aim is deploying multiple aerial BSs to cover a maximum number of users within a certain target area. To this end, we first propose a naive successive deployment method, which converts the non-convex constraints in the involved optimization into a combination of linear constraints through geometrical relaxation. Then, we investigate a deployment method based on K-means clustering. The method divides the target area into K convex subareas, where within each subarea, a mixed integer non-linear problem is solved. An iterative power efficient technique is further proposed to improve coverage probability with reduced power. Finally, we propose a robust technique for compensating the loss of coverage probability in the existence of inaccurate user location information. Our simulation results show that the proposed techniques achieve an up to 30% higher coverage probability when users are not distributed uniformly. In addition, the proposed simultaneous deployment techniques, especially the one using iterative algorithm, improve power-efficiency by up to 15% compared with the benchmark circle packing theory.

关键词： Unmanned aerial vehicles user coverage air-to-ground communication clustering algorithm

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