• Korean Journal of Computational Design and Engineering
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• v.16 no.4
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• pp.300-304
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• 2011

This paper proposes an approach to generate tool-path for laser pattern machining. Considering the mechanical structure of a laser pattern machine, it is quite similar to that of a 2D milling machine. Based on the observation, one may try to utilize the tool-path generation methodologies of 2D milling for the laser pattern machining. However, it is not possible to generate tool-path without considering the technological requirements of laser pattern machining which are different from those of 2D milling. In this paper, we identify the technological requirement of laser pattern machining, and propose a proper tool-path generation methodology to satisfy the technological requirements. For the efficient generation of tool-path, this paper proposes a tool-path element computation method, which is based on the concept of a monotone chain.

• Communications for Statistical Applications and Methods
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• v.10 no.2
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• pp.595-601
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• 2003

In this paper we propose an on line training method for classification based on least squares support vector machine. Proposed method enables the computation cost to be reduced and the training to be peformed incrementally, With the incremental formulation of an inverse matrix in optimization problem, current information and new input data can be used for building the new inverse matrix for the estimation of the optimal bias and Lagrange multipliers, so the large scale matrix inversion operation can be avoided. Numerical examples are included which indicate the performance of proposed algorithm.

• Proceedings of the Korean Society for Language and Information Conference
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• 1995.02a
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• pp.195-204
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• 1995

We propose a method to improve search time and space complexity in statistical machine translation architectures, by employing linguistic bracketing information on the source language sentence. It is one of the advantages of the probabilistic formulation that competing translations may be compared and ranked by a. principled measure, but at the same time, optimizing likelihoods over the translation spa.ce dictates heavy search costs. To make statistical architectures practical, heuristics to reduce search computation must be incorporated. An experiment applying our method to a prototype Chinese-English translation system demonstrates substantial improvement.

• Journal of Biosystems Engineering
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• v.5 no.2
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• pp.40-57
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• 1980

The aim of this study was to promote extensive use of strain gage for accurate measurement in the area of farm machinery research. The results are summarized as follows. 1. Although many strain gate transducers commercially available such as load-cells , accelerometers and pressure transducers have been used in the area of farm machinery research, many types of transducers had to be developed due to the inadaptability or high cost commercial transducers, in many instances. 2. A strain -gaged cantilever beam could be used as a good educational material to demonstrate the methods of Wheatstone bridge arrangement, calibration and theoreticval computation. A ring type load-cell and shaft torque transducer also could be used for the same purpose. 3. The torque and angular speed transducers for an auto-feed thresher and the displacement and pressure transducers for a rice whitener were made and gave satisfactory results. 4. Based on the above results, it is possible to develop simple and low-cost transducers to measure displacement, angular speed , torque and pressure of farm machine elements.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2542-2547
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• 2005

In this paper, we propose a support vector machine (SVM) hyper and kernel parameter optimization method which is based on minimizing radius/margin bound which is a kind of estimation of leave-one-error. This method uses dynamic encoding algorithm for search (DEAS) and gradient information for better optimization performance. DEAS is a recently proposed optimization algorithm which is based on variable length binary encoding method. This method has less computation time than genetic algorithm (GA) based and grid search based methods and better performance on finding global optimal value than gradient based methods. It is very efficient in practical applications. Hand-written letter data of MNI steel are used to evaluate the performance.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2001.04a
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• pp.28-33
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• 2001

Screw conveyors are used extensively in industrial for conveying and elevating materials. Despite their apparent simplicity, the mechanics of the conveying action is very complex. so many engineers depend on experiential data. Capacities of screw are pumping, steady flow of polymer melts, steady volumetric throughput etc. they are affected by geometry of screw, heat flux, pressure on inside barrel, rotating velocity, friction coefficient at screw surface etc. by computation volumetric efficiency increases as rotating velocity increases and decreases as friction coefficient increases. also it decreases with short pitch length. and double flight screw is more effective than single flight screw. The temperature of polymer melts by heating pad and injection pressure play a very important role in the injection molding machine. so in this paper we analyze thermal distortion and stress of screw includes pressure and temperature distributions by finite element analysis to understand what design factors influence on volumetric throughput efficiency of the screw and thermo-mechanical characteristics of screw.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• International journal of advanced smart convergence
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• v.9 no.2
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• pp.58-67
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• 2020

In this paper, we propose a data-driven-based beam selection scheme for massive multiple-input and multiple-output (MIMO) systems in ultra-dense networks (UDN), which is capable of addressing the problem of high computational cost of conventional coordinated beamforming approaches. We consider highly dense small-cell scenarios with more small cells than mobile stations, in the millimetre-wave band. The analog beam selection for hybrid beamforming is a key issue in realizing millimetre-wave UDN MIMO systems. To reduce the computation complexity for the analog beam selection, in this paper, two deep neural network models are used. The channel samples, channel gains, and radio frequency beamforming vectors between the access points and mobile stations are collected at the central/cloud unit that is connected to all the small-cell access points, and are used to train the networks. The proposed machine-learning-based scheme provides an approach for the effective implementation of massive MIMO system in UDN environment.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.792-793
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• 2015

This paper present a general computation taking into account the slotting effect on the magnetic field due to permanent magnet in surface mounted permanent magnet machine. The analytical method is based on the solution of Laplace's and Poisson's equations (by the separation of variables technique) for each subdomain, i.e., magnets, air gap, and slots. The general solution is obtained using boundary and continuity conditions. The analytical results are in very good agreement with those obtained by the FEA, considering both amplitude and waveform.

• Proceedings of the KIEE Conference
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• 1993.07a
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• pp.76-78
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• 1993

This paper describes an efficient method of computing any desired number of the most unstable eigenvalues and eigenvectors of a large scale multi-machine power system. Approximate eigenvalues obtained by Hessenberg process are refined using Rayleigh quotient iteration with cubic convergence property. If further eigenvalues and eigenvectors are needed, the procedure described above are repeated with deflation. The proposed algorithm can cover all the model types of synchronous machines, exciters, speed governing system and PSS defined in AESOPS. The proposed algorithm applied to New England test system with 10 machines and 39 buses produced the results same with AESOPS in faster computation time. Also eigenvectors computed in Rayleigh quotient iteration makes it possible to make eigen-analysis for improving unstable modes.