• Journal of Korean Institute of Industrial Engineers
• /
• v.24 no.1
• /
• pp.23-35
• /
• 1998

This article presents a network flow analysis to form flexible machine cells with minimum intercellular part moves and a neural network model to form part families. The operational sequences and production quantity of the part, and the number of cells and the cell size are taken into considerations for a 0-1 quadratic programming formulation and a network flow based solution procedure is developed. After designing the machine cells, a neural network approach for the integration of part families and the automatic assignment of new parts to the existing cells is proposed. A multi-layer backpropagation network with one hidden layer is used. Experimental results with varying number of neurons in hidden layer to evaluate the role of hidden neurons in the network learning performance are also presented. The comprehensive methodology developed in this article is appropriate for solving large-scale industrial applications without building the knowledge-based expert rule for the cellular manufacturing environment.

• Journal of KIISE
• /
• v.43 no.10
• /
• pp.1085-1093
• /
• 2016

Community-based Question Answering system is a system which provides answers for each question from the documents uploaded on web communities. In order to enhance the capacity of question analysis, former methods have developed specific rules suitable for a target region or have applied machine learning to partial processes. However, these methods incur an excessive cost for expanding fields or lead to cases in which system is overfitted for a specific field. This paper proposes a multiple machine learning method which automates the overall process by adapting appropriate machine learning in each procedure for efficient processing of community-based Question Answering system. This system can be divided into question analysis part and answer selection part. The question analysis part consists of the question focus extractor, which analyzes the focused phrases in questions and uses conditional random fields, and the question type classifier, which classifies topics of questions and uses support vector machine. In the answer selection part, the we trains weights that are used by the similarity estimation models through an artificial neural network. Also these are a number of cases in which the results of morphological analysis are not reliable for the data uploaded on web communities. Therefore, we suggest a method that minimizes the impact of morphological analysis by using character features in the stage of question analysis. The proposed system outperforms the former system by showing a Mean Average Precision criteria of 0.765 and R-Precision criteria of 0.872.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.60 no.4
• /
• pp.181-185
• /
• 2011

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop current harmonics estimation method based on Extreme Learning Machine (ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate current harmonics for various input voltage.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.61 no.1
• /
• pp.29-34
• /
• 2012

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop a load modeling method based on Extreme Learning Machine(ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate active and reactive powers than conventional methods.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.62 no.4
• /
• pp.169-174
• /
• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.25 no.1
• /
• pp.97-104
• /
• 2015

An 'associative cube', a class of auto-associative memories, is revisited here, in which training data and hidden orthogonal basis functions such as wavelet packets or Fourier kernels, are combined in the weight cube. This weight cube has hidden units in its depth, represented by a three dimensional cubic structure. We develop an unsupervised incremental learning mechanism based upon the adaptive least squares method. Training data are mapped into orthogonal basis vectors in a least-squares sense by updating the weights which minimize an energy function. Therefore, a prescribed orthogonal kernel is incrementally assigned to an incoming data. Next, we show how a decoding procedure finds the closest one with a competitive network in the hidden layer. As noisy test data are applied to an associative cube, the nearest one among the original training data are restored in an optimal sense. The simulation results confirm robustness of associative cubes even if test data are heavily distorted by various types of noise.

• Korean Management Science Review
• /
• v.16 no.1
• /
• pp.157-171
• /
• 1999

In a increasingly competitive marketplace, the manufacturing companies have no choice but looking for ways to improve productivity to sustain their competitiveness and survive in the industry. Recently cellular manufacturing has been under discussion as an option to be easily implemented without burdensome capital investment. The objective of cellular manufacturing is to realize many aspects of efficiencies associated with mass production in the less repetitive job-shop production systems. The very first step for cellular manufacturing is to group the sets of parts having similar processing requirements into part families, and the equipment needed to process a particular part family into machine cells. The underlying problem to determine the part and machine assignments to each manufacturing cell is called the cell formation. The purpose of this study is to develop a clustering algorithm based on the neural network approach which overcomes the drawbacks of ART1 algorithm for cell formation problems. In this paper, a generalized learning vector quantization(GLVQ) algorithm was devised in order to transform a 0/1 part-machine assignment matrix into the matrix with diagonal blocks in such a way to increase clustering performance. Furthermore, an assignment problem model and a rearrangement procedure has been embedded to increase efficiency. The performance of the proposed algorithm has been evaluated using data sets adopted by prior studies on cell formation. The proposed algorithm dominates almost all the cell formation reported so far, based on the grouping index($\alpha$ = 0.2). Among 27 cell formation problems investigated, the result by the proposed algorithm was superior in 11, equal 15, and inferior only in 1.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.509-521
• /
• 2016

A novel Samples Balanced Genetic Algorithm combined with Extreme Learning Machine (SBGA-ELM) for Parkinson's Disease diagnosis and detecting bio-markers is presented in this paper. Proposed approach uses genes' expression data of 22,283 genes from open source ParkDB data base for accurate PD diagnosis and detecting bio-markers. Proposed SBGA-ELM includes two major steps: feature (genes) selection and classification. Feature selection procedure is based on proposed Samples Balanced Genetic Algorithm designed specifically for genes expression data from ParkDB. Proposed SBGA searches a robust subset of genes among 22,283 genes available in ParkDB for further analysis. In the "classification" step chosen set of genes is used to train an Extreme Learning Machine (ELM) classifier for an accurate PD diagnosis. Discovered robust subset of genes creates ELM classifier with stable generalization performance for PD diagnosis. In this research the robust subset of genes is also used to discover 24 bio-markers probably responsible for Parkinson's Disease. Discovered robust subset of genes was verified by using existing PD diagnosis approaches such as SVM and PBL-McRBFN. Both tested methods caused maximum generalization performance.

• Journal of Digital Contents Society
• /
• v.17 no.6
• /
• pp.565-579
• /
• 2016

In this paper a novel cancer subtype's classifier based on Hybrid Samples Balanced Genetic Algorithm with Extreme Learning Machine (hSBGA-ELM) is presented. Proposed cancer subtype's classifier uses genes' expression data of 16063 genes from open Global Cancer Map (GCM) data base for accurate cancer subtype's classification. Proposed method efficiently classifies 14 subtypes of cancer (breast, prostate, lung, colorectal, lymphoma, bladder, melanoma, uterus, leukemia, renal, pancreas, ovary, mesothelioma and CNS). Proposed hSBGA-ELM unifies genes' selection procedure and cancer subtype's classification into one framework. Proposed Hybrid Samples Balanced Genetic Algorithm searches a reduced robust set of genes responsible for cancer subtype's classification from 16063 genes available in GCM data base. Selected reduced set of genes is used to build cancer subtype's classifier using Extreme Learning Machine (ELM). As a result, reduced set of robust genes guarantees stable generalization performance of the proposed cancer subtype's classifier. Proposed hSBGA-ELM discovers 95 genes probably responsible for cancer. Comparison with existing cancer subtype's classifiers clear indicates efficiency of the proposed method.

• Analytical Science and Technology
• /
• v.24 no.6
• /
• pp.533-543
• /
• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.