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IEEE International Congress on Cybermatics / IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology

作者： Loh, Woong-Kee Kim, Young-Kuk Gachon Univ Dept Software Seongnam South Korea Chungnam Natl Univ Dept Comp Sci & Engn Daejeon South Korea

ISBN: (纸本)9781538679753

Since the first introduction of integrated circuits (ICs), there has been dramatic improvements in semiconductor manufacturing technologies. To minimize even slightest faults in manufacturing processes, many studies are being conducted to systematically analyze the causes of faults. In this paper, we propose an algorithm that finds the causes of faults by clustering the fault detection results. Our experiments show that our algorithm forms good clusters of runs having similar sources of faults.

关键词： fault analysis sensors semiconductor manufacturing processes clustering algorithm

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Distributed Mobile Group Detection algorithms: Application to Cycling Race 39

Distributed Mobile Group Detection Algorithms: Application t...

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39th Annual IEEE Conference on Local Computer Networks (LCN)

作者： Lauzier, Matthieu Fraboulet, Antoine Gorce, Jean-Marie Risset, Tanguy Univ Lyon INRIA CITI INRIA INSA Lyon F-69621 Villeurbanne France HIKOB SA F-69603 Villeurbanne France Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

ISBN: (纸本)9781479937844

In the large research area of wireless body area networks, cooperative applications involving several users is attracting strong interests. This cooperation may target a simple information exchange or even some cooperative decision such as swarm coordination. We consider in this paper such a swarm of users moving in a common direction and we are interested in the mechanisms allowing to propagate and share some common information. We extend and improve a previous algorithm derived as a max-consensus approach. We describe a complete experimental setup deployed during a real bike race with 200 runners. We provide a deep analysis of the radio links properties (stability, strength...) and the equivalent graph evolution (graph diameter, neighborhood characterization...) from the record made on 20 nodes. At the best of our knowledge, this paper is the first one providing such analysis in a real experiment with a swarm of 200 nodes located on bikes during an official race.

关键词： mobile WSN distributed consensus clustering algorithm cycling race monitoring ad-hoc network

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RETRACTED: Audio-Visual Automatic Speech Recognition Towards Education for Disabilities (Retracted Article)

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JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS 2023年第9期53卷 3581-3594页

作者： Debnath, Saswati Roy, Pinki Namasudra, Suyel Crespo, Ruben Gonzalez Alliance Univ Dept Comp Sci & Engn Bangalore Karnataka India Natl Inst Technol Dept Comp Sci & Engn Silchar Assam India Natl Inst Technol Patna Dept Comp Sci & Engn Patna Bihar India Univ Int La Rioja Logrono Spain

Education is a fundamental right that enriches everyone's life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition.

关键词： AV-ASR LBP-TOP GLCM MFCC clustering algorithm Supervised learning

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Optimal Control for Complex Systems under Multi-granularity Based on Data Mining 31

Optimal Control for Complex Systems under Multi-granularity ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Xu, Zhengguang Guo, Lingli Wang, Mushu Lu, Yanrong Univ Sci & Technol Beijing Sch Automat & Elect Engn Beijing 100083 Peoples R China Changzhi Coll Dept Math Changzhi 046011 Peoples R China

ISBN: (纸本)9781728101057

This paper proposes a control scheme for a kind of complex systems whose dynamics follow statistical law. First, inspired by data mining technology, the samples are clustered into several classes reflecting the working pattern by modified ISODATA method. Second, based on the clustering result, the cell state space is constructed within global and local target cells respectively by Cartesian product, and the cell mapping further is created via the analysis of pattern time series. Third, by the reverse searching algorithm, global optimal paths to the target cell are found and the information provided by the searching paths constitute the optimal controller at a coarse-grained level so as to bring the system into the target cell quickly, once enter into the target cell, another controller which consists of the subcell mapping with the optimal cost increment in refined granularity level is applied in order to reduce steady-state error. Finally, a simulation in the dynamic trajectory of a sintering process is given to demonstrate the feasibility of the proposed approach.

关键词： Data mining clustering algorithm Multi-granularity Cell mapping

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An Energy-aware Routing algorithm for Heterogeneous Wireless Sensor Networks

An Energy-aware Routing Algorithm for Heterogeneous Wireless...

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9th International Conference on Hybrid Intelligent Systems (HIS 2009)

作者： Liang, Ying Feng, Yongxin Shenyang Ligong Univ Sch Informat Sci & Engn Shenyang Liaoning Prov Peoples R China

ISBN: (纸本)9780769537450

In order to prolong the network lifetime, energy-aware protocols should be designed to adapt the characteristic of wireless sensor networks. clustering is a kind of key routing technique used to reduce energy consumption. In this paper, a new clustering algorithm MNLC (Maximum Network Lifetime clustering algorithm) for energy heterogeneous sensor networks is proposed. The algorithm uses distributed, dynamic clustering process. On the one hand, cluster-head selection is primarily determined by the residual energy level of each node, on the other hand, the ordinary nodes select which cluster to join according to the remaining energy level of the candidate cluster-heads and the parameter of communication cost in a cluster, so as to effectively achieve the balanced distribution of energy loss in the network. Simulation results show that MNLC could better implement load balance and prolong the network lifetime in heterogeneous environments.

关键词： wireless sensor network clustering algorithm energy heterogeneous load balancing

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A Novel Framework to Elucidate Core Classes in a Dataset

A Novel Framework to Elucidate Core Classes in a Dataset

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2010 IEEE World Congress on Computational Intelligence

作者： Soria, Daniele Garibaldi, Jonathan M. Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9781424481262

In this paper we present an original framework to extract representative groups from a dataset, and we validate it over a novel case study. The framework specifies the application of different clustering algorithms, then several statistical and visualisation techniques are used to characterise the results, and core classes are defined by consensus clustering. Classes may be verified using supervised classification algorithms to obtain a set of rules which may be useful for new data points in the future. This framework is validated over a novel set of histone markers for breast cancer patients. From a technical perspective, the resultant classes are well separated and characterised by low, medium and high levels of biological markers. Clinically, the groups appear to distinguish patients with poor overall survival from those with low grading score and better survival. Overall, this framework offers a promising methodology for elucidating core consensus groups from data.

关键词： biological marker breast cancer patient cancer clustering algorithm consensus clustering core class elucidation data handling data visualisation grading score histone marker medical diagnostic computing pattern classification pattern clustering representative group statistical analysis statistical technique supervised classification algorithm visualisation technique

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Improving K-Means Through Better Initialization And Normalization 5

Improving K-Means Through Better Initialization And Normaliz...

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International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Choudhary, Akanksha Sharma, Prashant Singh, Manoj Gurukul Inst Engn & Technol Dept Comp Sci Kota Rajasthan India

ISBN: (纸本)9781509020287

K-means is still a popular clustering algorithm and active research area. The research is majorly focused at improving efficiency and effectiveness of the method. This paper proposes combined approach of a ranked initialization and normalization of data values with k-means. Three variations of a score based initialization approach is proposed. Experiments are performed on normalized data to prove the superiority of the proposed algorithm.

关键词： clustering algorithm k-means initialization normalization

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Weld Defect Location Method of U-Shaped Crane Boom Based on Helical Guided Waves 10th

Weld Defect Location Method of U-Shaped Crane Boom Based on ...

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10th European Workshop on Structural Health Monitoring (EWSHM)

作者： Lu, Zhaojing Liu, Zenghua Jiang, Wenshuo Chen, Honglei Beijing Univ Technol Fac Mat & Mfg Beijing 100124 Peoples R China Fudan Univ Acad Engn & Technol Shanghai 200433 Peoples R China

ISBN: (纸本)9783031073229;9783031073212

The boom is the key load-bearing component of the crane, and its health seriously threatens the service performance of the crane. In order to ensure the safe production of cranes, nondestructive testing (NDT) and structural health monitoring (SHM) of boom become more and more urgent and important. In this paper, the intelligent defect location algorithm based on helical guided waves is applied to the weld defect detection of U-shaped boom. Intelligent defect location algorithm is an imaging algorithm that combines ellipse imaging principle, evolutionary algorithm and K-means clustering algorithm. This paper verifies the effectiveness of this algorithm for weld defect location of U-shaped boom through experimental research. Firstly, the propagation regularity of helical guided waves in the U-shaped boom structure is studied. In addition, the elliptical imaging algorithm is used to image and analyze the weld defects in the U-shaped boom, and the location of the defects is preliminarily determined. The defect location analysis of weld defects is carried out by using the intelligent defect location algorithm. By comparing the imaging results of the two methods, it is found that the intelligent defect location algorithm has higher resolution and can effectively improve the defect location accuracy. This algorithm provides a tool for health monitoring of special-shaped structures based on guided waves.

关键词： Structural health monitoring U-shaped crane boom Helical guided waves Evolutionary algorithm clustering algorithm

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Using an algorithmic approach for grouping roles and sub-units

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JOURNAL OF ORGANIZATION DESIGN 2020年第1期9卷 1-19页

作者： Worren, Nicolay Christiansen, Tore Soldal, Kim Verner Norwegian Univ Life Sci NMBU Sch Econ & Business POB 5003 N-1432 As Norway Inkubator As Reconfig A 5 Postboks 19 N-1431 As Norway

A key task in organization design is to group elements (e.g., roles) into sub-units (e.g., teams or departments). This task is computationally challenging as one must take into consideration a potentially large number of interdependencies between the elements. It also requires data about work processes in the organization, which are rarely present. We have initiated a research program that aims at developing a tool-Reconfig-to improve grouping decisions. It first collects data from employees about their working relationships (i.e., interdependencies) and then uses a computer algorithm to cluster the data in the most optimal manner. The clustered solution represents the formal structure that minimizes coordination costs by grouping the most highly interdependent elements together. We describe the tool, report on two pilot applications, and discuss both the future potential and limitations of the approach.

关键词： Organization design clustering algorithm Design structure matrix (DSM) Interdependencies Software tool

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Exploring success factors of tourism performing arts by analyses of online reviews

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JOURNAL OF HOSPITALITY AND TOURISM TECHNOLOGY 2023年第1期14卷 37-52页

作者： Cui, Yuan Kim, Seungwoon Feng, Shi Jeonbuk Natl Univ Dept Business Adm Jeonju South Korea Northeastern Univ Dept Comp Sci Shenyang Peoples R China

PurposeThis study aims to explore the success factors of tourism performing arts (TPA) programs by analyzing a large data set of online reviews. Design/methodology/approachA total of 195,230 reviews from *** were collected and preprocessed. A deep learning method was leveraged to estimate the similarity between words. Then, regression analysis was conducted to determine success factors. FindingsThis study extracted four positive and two negative factors affecting tourist satisfaction with tourism performance arts. The results demonstrate that the tourists paid the most attention to the traditional Chinese cultural aspects, audiovisual effects and the actors' performing enthusiasm. Research limitations/implicationsDespite this study's large data set, the focused was only on Chinese reviews. It would be useful and interesting to compare the success factors of tourism performance arts programs offered in different countries. Practical implicationsThe study findings can contribute to the development of TPA programs to attract tourists to travel destinations. Originality/valueThis study demonstrates that analyzing online reviews of TPA through text mining technology is an effective method of understanding tourist satisfaction.

关键词： Tourism performing arts Success factor Tourist satisfaction Text mining Tourism online review clustering algorithm ??? ???? ???? ????? ??? ?????? ????

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