Hierarchical clustering approach for determination of isomorphism among planar kinematic chains and their derived mechanisms

Lohumi, M. K.; Mohammad, Aas; Khan, Irshad

doi:10.1007/s12206-012-0902-9

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…Step 1: Retrieve and compare the structural parameters of these two KCs, they are the same [15,27,212,13,6,0,0,6,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0].…”

Section: Examples Of Isomorphism Identificationmentioning

confidence: 99%

“…In 2012, Lohumi et al 27 proposed an expression for KC with weighted square shortest path distance matrix firstly, and turned into dendrogram graph with hierarchical clustering algorithm, inheritance relative coefficients of dendrogram graph were used as the basis of KC isomorphic identification. Hierarchical clustering approach was a widely used method, in which all samples were classified, and the distance between classes were defined, then two classes with smallest distance were merged into a new class, the above process should be implemented till only one class merged at last.…”

Section: Introductionmentioning

confidence: 99%

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An isomorphism identification method of kinematic chain based on optimal arrangement and comparison of branch-chain matrix derived from dendrogram graph

Sun

Liu

et al. 2022

Advances in Mechanical Engineering

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Isomorphism identification is an essential step in the structure synthesis of Kinematic Chain (KC), and needs a large amount of analysis and calculation. To find an isomorphism identification method with simple rules, scientific feasibility and less analysis and calculation has always been a research hotspot of mechanism scholars. In this paper, the structure information of KC is described by dendrogram structure graph with multiple joints, and Branch-chain Matrix (BM) is separated from the dendrogram structure. The characteristics of BM are analyzed, and the concepts of intimacy between branch-chains and Repeatability Matrix (RM) corresponding to BM are proposed. Based on fact that both dendrogram graph and BM can uniquely determine the structural information of one KC, a new isomorphism identification method for KC, based on row optimal rearrangement and comparison of BM, is proposed. The operation steps are discussed in detail, and several cases are analyzed to show this method has advantages such as easy rules, small calculation of retrieval and comparison, and easy to be programmed.

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“…Step 1: Retrieve and compare the structural parameters of these two KCs, they are the same [15,27,212,13,6,0,0,6,0,0,0,3,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0].…”

Section: Examples Of Isomorphism Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An isomorphism identification method of kinematic chain based on optimal arrangement and comparison of branch-chain matrix derived from dendrogram graph

Sun

Liu

et al. 2022

Advances in Mechanical Engineering

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“…The number of groups is controlled by the thresholds of main variables. The threshold represents the critical range for each variable in a group [39]. If the ranges of all the variables in a group are smaller than the threshold vector, then the clustering stops.…”

Section: Establishment Of a Prediction Model For Endpoint Sulfur Contentmentioning

confidence: 99%

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

Yang

Zhang

et al. 2020

IEEE Access

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In the present work, the endpoint sulfur content prediction model of Kambara Reactor (KR) desulfurization in the steelmaking process is investigated. For Artificial Neural Network (ANN), the effects of different structure parameters, including the number of hidden layer neurons, activation functions and training functions, on the performance of desulfurization model are studied. The initial weights and biases of the neural network is optimized to further elevated the prediction accuracy of the model. Three models established by using Multiple Linear Regression (MLR), ANN and a hybrid algorithm (artificial neural network optimized by SAPSO, named SAPSO-ANN) are compared by the Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Relative Error (MARE). The results show that in the process of KR desulfurization, the nonlinear model of ANN and SAPSO-ANN has a higher accuracy than the linear model of MLR. Among the three models, the SAPSO-ANN model achieves the highest accuracy with R value of 0.54, RMSE of 2.61×10 −4 % and MAER of 0.47, which is selected to analyze the effect of process parameters on the desulfurization rate and design the amount of desulfurization flux in the KR desulfurization process. Experimental results show good agreements with the calculation results, indicating the practicability of the model. INDEX TERMS Artificial neural network, particle swarm optimization, multiple linear regression, prediction of endpoint sulfur content, KR desulfurization process.

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“…The experimental results reached 100% classification rate. Lohumi and Mohammad [8] proposed a hierarchical clustering algorithm to identify the different institutions derived from the kinematic chain and learn the isomorphism between the kinematic chain and its derivatives. The kinematic chain was represented as a weighted squared shortest path distance matrix, which is further transformed in the form of tree or dendrogram using a hierarchical clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Cycle Condition Identification of Loader Based on Optimized KNN Algorithm

et al. 2020

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The working conditions of loaders alternate between stages of full or empty loads, loading or unloading, and moving forward or backward, which complicates the vehicle's characteristic response. Based on the K-nearest neighbor (KNN) algorithm and a principal component analysis (PCA) method, stages recognition algorithm under the V-type working conditions of a loader was studied. First, the collected transmission signals were noise-reduced and filtered. Second, the PCA was used to reduce the dimensions of the data. Finally, the working condition samples were established from the data obtained, which were later trained and classified using the KNN algorithm. Compared with the neural network algorithm, the accuracy of the optimized KNN algorithm reaches 99.4%, and its running time is 2.1s. The algorithm described in this paper guarantees a high accuracy under recognition of the loader conditions in a short running time. It can be extended to an intelligent identification using big data and artificial intelligence control of the construction machinery. INDEX TERMS K-nearest neighbor (KNN), loader, principal component analysis (PCA), condition identification.

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Hierarchical clustering approach for determination of isomorphism among planar kinematic chains and their derived mechanisms

Cited by 11 publications

References 16 publications

An isomorphism identification method of kinematic chain based on optimal arrangement and comparison of branch-chain matrix derived from dendrogram graph

An isomorphism identification method of kinematic chain based on optimal arrangement and comparison of branch-chain matrix derived from dendrogram graph

Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

Cycle Condition Identification of Loader Based on Optimized KNN Algorithm

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