• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.807-812
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• 2007

Recently, Extreme learning machine(ELM), a novel learning algorithm which is much faster than conventional gradient-based learning algorithm, was proposed for single-hidden-layer feedforward neural networks. The initial input weights and hidden biases of ELM are usually randomly chosen, and the output weights are analytically determined by using Moore-Penrose(MP) generalized inverse. But it has the difficulties to choose initial input weights and hidden biases. In this paper, an advanced method using the bacterial foraging algorithm to adjust the input weights and hidden biases is proposed. Experiment at results show that this method can achieve better performance for problems having higher dimension than others.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.3
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• pp.535-541
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• 2009

In this paper, we present the improvement on the performance of driving part for single-phase MJ81 switch point machine which has been developed for localization. The single-phase motor's specification and reliability for speed and safety improvement of conventional line was investigated in "Development project for Speed-up on Conventional Line" We systemized the test procedure fur single-phase motor by investigating the feasibility for localization and the specification of function and performance. Also, we developed appropriate technology and proved the durability of the single-phase driving motor by executing synthesis test over 200,000 times.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.7
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• pp.276-281
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• 2007

In this paper, we use Support Vector Machine to predict the susceptibility of chronic hepatitis from single nucleotide polymorphism data. Our data set consists of SNP data for 328 patients based on 28 SNPs and patients classes(chronic hepatitis, healthy). We use leave-one-out cross validation method for estimation of the accuracy. The experimental results show that SVM with SNP is capable of classifying the SNP data successfully for chronic hepatitis susceptibility with accuracy value of 67.1%. The accuracy of all SNPs with health related feature(sex, age) is improved more than 7%(accuracy 74.9%). This result shows that the accuracy of predicting susceptibility can be improved with health related features. With more SNPs and other health related features, SVM prediction of SNP data is a potential tool for chronic hepatitis susceptibility.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.169-180
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• 2015

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.

• Journal of KIISE
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• v.41 no.10
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• pp.762-773
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• 2014

As the web of data is increasingly producing large RDFS datasets, it becomes essential in building scalable reasoning engines over large triples. There have been many researches used expensive distributed framework, such as Hadoop, to reason over large RDFS triples. However, in many cases we are required to handle millions of triples. In such cases, it is not necessary to deploy expensive distributed systems because logic program based reasoners in a single machine can produce similar reasoning performances with that of distributed reasoner using Hadoop. In this paper, we propose a scalable RDFS reasoner using logical programming methods in a single machine and compare our empirical results with that of distributed systems. We show that our logic programming based reasoner using a single machine performs as similar as expensive distributed reasoner does up to 200 million RDFS triples. In addition, we designed a meta data structure by decomposing the ontology triples into separate sectors. Instead of loading all the triples into a single model, we selected an appropriate subset of the triples for each ontology reasoning rule. Unification makes it easy to handle conjunctive queries for RDFS schema reasoning, therefore, we have designed and implemented RDFS axioms using logic programming unifications and efficient conjunctive query handling mechanisms. The throughputs of our approach reached to 166K Triples/sec over LUBM1500 with 200 million triples. It is comparable to that of WebPIE, distributed reasoner using Hadoop and Map Reduce, which performs 185K Triples/sec. We show that it is unnecessary to use the distributed system up to 200 million triples and the performance of logic programming based reasoner in a single machine becomes comparable with that of expensive distributed reasoner which employs Hadoop framework.

• Proceedings of the KIEE Conference
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• summer
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• pp.866-868
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• 2004

The flux-reversal machine(FRM) is a new brushless doubly-salient permanent-magnet machine combining the advantages of the switched-reluctance machine(SRM) and the permanent-magnet machine(PMM) into one machine. FRM has a naturally low inductance, therefore, a low electrical time constant. This feature, combined with its simple construction and low rotor inertia appear to make the FRM attractive as a low-cost high-speed machine. For high-speed applications, two alternative commutation strategies are studied, one using the phase commutation advancing technique and another using the conducting pulse-width control. This paper describes the techniques and reports the corresponding simulated and experimented performance

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.4
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• pp.755-760
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• 2019

In this paper, we developed the multi-tool carving machine which integrates the existing hot-wire carving machine, hot-wire cutting machine and spindle so that the shape of complex structure can be produced easily and quickly. We have also developed software that solves the problem that G-Code applies only to a single tool, and controls the details of the machine's operations that can not be managed with existing 3D modeling tools.

• Industrial Engineering and Management Systems
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• v.3 no.2
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• pp.100-115
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• 2004

The network representation and branch and bound algorithm with efficient lower and upper bounding procedures are developed to determine a global optimal production schedule on a machine that minimizes sequence-dependent setup cost and earliness/tardiness penalties. Lower bounds are obtained based on heuristic and Lagrangian relaxation. Priority dispatching rule with local improvement procedure is used to derive an initial upper bound. Two dominance criteria are incorporated in a branch and bound procedure to reduce the search space and enhance computational efficiency. The computational results indicate that the proposed procedure could optimally solve the problem with up to 40 jobs in a reasonable time using a personal computer.

• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.62-67
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• 1998

An arc welding machine using single switched PFC converter is presented in this paper. First, the basic operation and principle is reviewed. Controller design is intended to force voltage ripple to minimize, and dynamic response to enhance, Feed-forward strategy for arc welding machine is developed, and that is verified by simulation. The improved power factor characteristics of arc welding machine known as low power factor system with nonlinear property, are shown and evaluated compared to conventional one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1440-1446
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• 2008

In this paper, a novel TMR (Triple Modular Redundancy) memory structure is proposed using state feedback control of asynchronous sequential machines. The main ability of the proposed structure is to correct the fault of SEU (Single Event Upset) asynchronously without resorting to the global synchronous clock. A state-feedback controller is combined with the TMR realized as a closed-loop asynchronous machine and corrective behavior is operated whenever an unauthorized state transition is observed so as to recover the failed state of the asynchronous machine to the original one. As a case study, an asynchronous machine modelling of TMR and the detailed procedure of controller construction are presented. A simulation results using VHDL shows the validity of the proposed scheme.