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Data Clustering Using Hybrid Neural Network
( Donghai Guan ),( Andrey Gavrilov ),( Weiwei Yuan ),( Sungyoung Lee ),( Young-koo Lee ) 한국정보처리학회 2007 한국정보처리학회 학술대회논문집 Vol.14 No.1
Clustering plays an indispensable role for data analysis. Many clustering algorithms have been developed. However, most of them suffer poor performance of learning. To archive good clustering performance, we develop a hybrid neural network model. It is the combination of Multi-Layer Perceptron (MLP) and Adaptive Resonance Theory 2 (ART2). It inherits two distinct advantages of stability and plasticity from ART2. Meanwhile, by combining the merits of MLP, it improves the performance for clustering. Experiment results show that our model can be used for clustering with promising performance.
The Role of Reputation in Ubiquitous Healthcare System
( Weiwei Yuan ),( Donghai Guan ),( Sungyoung Lee ) 한국정보처리학회 2007 한국정보처리학회 학술대회논문집 Vol.14 No.1
In this work, we analyze the role of reputation in ubiquitous healthcare system as well as the relationship of security, trust and reputation in this environment in details. In addition, an example is given to show how to use reputation system in ubiquitous healthcare and how to use reputation system on decision making.
Nearest neighbor editing aided by unlabeled data
Guan, Donghai,Yuan, Weiwei,Lee, Young-Koo,Lee, Sungyoung Elsevier 2009 Information sciences Vol.179 No.13
<P><B>Abstract</B></P><P>This paper proposes a novel method for nearest neighbor editing. Nearest neighbor editing aims to increase the classifier’s generalization ability by removing noisy instances from the training set. Traditionally nearest neighbor editing edits (removes/retains) each instance by the voting of the instances in the training set (labeled instances). However, motivated by semi-supervised learning, we propose a novel editing methodology which edits each training instance by the voting of all the available instances (both labeled and unlabeled instances). We expect that the editing performance could be boosted by appropriately using unlabeled data. Our idea relies on the fact that in many applications, in addition to the training instances, many unlabeled instances are also available since they do not need human annotation effort. Three popular data editing methods, including edited nearest neighbor, repeated edited nearest neighbor and All <I>k</I>-NN are adopted to verify our idea. They are tested on a set of UCI data sets. Experimental results indicate that all the three editing methods can achieve improved performance with the aid of unlabeled data. Moreover, the improvement is more remarkable when the ratio of training data to unlabeled data is small.</P>
Zhiping Zhai,Donghai Yuan,Yuezheng Lan,Haixu Zhao 대한기계학회 2023 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.37 No.10
During the operation of a forage crusher, the common issues usually faced are shorter mean time between failures and low reliability. The hammer rotor, a critical component, is prone to fatigue fracture, hammer wear, violent vibration of the rotor system caused by uneven wear of the hammers, and other issues that reduce the machine's service life and reliability. In order to avoid failure modes within the design life of the forage crusher and improve its reliability, the functional functions of the fatigue fracture failure, hammer wear failure, and resonance failure modes were established, the marginal distribution functions for each individual failure mode were computed, and the reliability model of the hammer rotor under multiple failure modes is constructed based on the correlation degree between the failure modes. On this basis, the reliability of the forage crusher is improved by optimizing the structure and working parameters. Before optimization, the fatigue reliability, wear reliability, and vibration reliability of the hammer rotor are 0.878, 0.94, and 0.248, respectively, and the reliability of the hammer rotor under multiple failure modes is 0.2116. After optimization, the fatigue, wear, and vibration reliability are 0.979, 0.9997, and 0.932, respectively. The reliability of the hammer rotor under multiple failure modes is 0.923, which reduces the probability of failure within the design life and meets the requirement that the reliability of key parts of agricultural and animal husbandry machinery is not less than 90 %. This study validates the reliability models and multi-objective optimization results and serves as a reference for forage crusher structural reliability design and optimization.