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昇 大隅

Bio: 昇 大隅 is an academic researcher. The author has contributed to research in topics: Fuzzy classification & Fuzzy set operations. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

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01 Jan 1979

7 citations


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Journal ArticleDOI
TL;DR: An adaptive hierarchical fuzzy clustering algorithm that generates a hierarchy in which each node is split into a variable number of sub-nodes, determined by an innovative quality assessment of soft clusters based on the evaluation of multiple dimensions such as the cluster's cohesion, its cardinality, its mass, and its fuzziness.
Abstract: In this paper an adaptive hierarchical fuzzy clustering algorithm is presented, named Hierarchical Data Divisive Soft Clustering (H2D-SC). The main novelty of the proposed algorithm is that it is a quality driven algorithm, since it dynamically evaluates a multi-dimensional quality measure of the clusters to drive the generation of the soft hierarchy. Specifically, it generates a hierarchy in which each node is split into a variable number of sub-nodes, determined by an innovative quality assessment of soft clusters, based on the evaluation of multiple dimensions such as the cluster's cohesion, its cardinality, its mass, and its fuzziness, as well as the partition's entropy. Clusters at the same hierarchical level share a minimum quality value: clusters in the lower levels of the hierarchy have a higher quality; this way more specific clusters (lower level clusters) have a higher quality than more general clusters (upper level clusters). Further, since the algorithm generates a soft partition, a document can belong to several sub-clusters with distinct membership degrees. The proposed algorithm is divisive, and it is based on a combination of a modified bisecting K-Means algorithm with a flat soft clustering algorithm used to partition each node. The paper describes the algorithm and its evaluation on two standard collections.

31 citations

01 Jan 2016
TL;DR: A new framework, namely DTW-kNN, is introduced, to recognize and classify three important surgical tasks including suturing, needle passing and knot tying based on kinematic data captured using da Vinci robotic surgery system.
Abstract: COMPUTATIONAL MODELING APPROACHES FOR TASK ANALYSIS IN ROBOTIC-ASSISTED SURGERY by MAHTAB JAHANBANI FARD May 2016 Advisor: Dr. R. Darin Ellis Major: Industrial Engineering Degree: Doctor of Philosophy Surgery is continuously subject to technological innovations including the introduction of robotic surgical devices. The ultimate goal is to program the surgical robot to perform certain difficult or complex surgical tasks in an autonomous manner. The feasibility of current robotic surgery systems to record quantitative motion and video data motivates developing descriptive mathematical models to recognize, classify and analyze surgical tasks. Recent advances in machine learning research for uncovering concealed patterns in huge data sets, like kinematic and video data, offer a possibility to better understand surgical procedures from a system point of view. This dissertation focuses on bridging the gap between these two lines of the research by developing computational models for task analysis in robotic-assisted surgery. The key step for advance study in robotic-assisted surgery and autonomous skill assess- ment is to develop techniques that are capable of recognizing fundamental surgical tasks intelligently. Surgical tasks and at a more granular level, surgical gestures, need to be quan- tified to make them amenable for further study. To answer to this query, we introduce a new framework, namely DTW-kNN, to recognize and classify three important surgical tasks including suturing, needle passing and knot tying based on kinematic data captured using da Vinci robotic surgery system. Our proposed method needs minimum preprocessing that

18 citations

Journal ArticleDOI
TL;DR: This paper is providing a heterogeneous cluster ensemble approach to improve the stability of fuzzy cluster analysis by applying different fuzzy clustering algorithms on the datasets obtaining multiple partitions, which in the later stage will be fused into the final consensus matrix.

10 citations

Proceedings ArticleDOI
01 Dec 2008
TL;DR: Two hybrid prediction models based on BP neural network, ES (exponential smoothing) and FCM (Fuzzy C-Means) clustering are proposed to predict the possible rate and ages of smokers suffering the lung cancer.
Abstract: Recent researches show that lung cancer owns actual dose-response relationship with calendar-year smoking environment exposure matrix and individual medical record. In this paper, two hybrid prediction models based on BP neural network, ES (exponential smoothing) and FCM (Fuzzy C-Means) clustering are proposed to predict the possible rate and ages of smokers suffering the lung cancer. The BP-ES (Exponential Smoothing) model can exert the superiorities of the time series datum of smoking crowds and other pathogenic factors; and the BPFCM clustering model can reduce the parameter amount and complexity of BP netpsilas training greatly. The experiments show that the accuracy of the hybrid models are enhanced greatly contrasted with single BP neural network, and can work as effective methods for the statistic, analysis and prediction to lung cancer.

3 citations