• Journal of Institute of Control, Robotics and Systems
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• v.10 no.10
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• pp.855-868
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• 2004

There are several types of singularities in controlling robotic manipulators: kinematic singularity, algorithmic singularity, semi-kinematic singularity, semi-algorithmic singularity, and representation singularity. The kinematic and algorithmic singularities have been investigated intensively because they are not predictable or difficult to avoid. The problem with these singularities is an unnecessary performance reduction in non-singular region and the difficulty in performance tuning. Tn this paper, we propose a method of avoiding kinematic and algorithmic singularities by applying a task reconstruction approach while maximizing the task performance by calculating singularity measures. The proposed method is implemented by removing the component approaching the singularity calculated by using singularity measure in real time. The outstanding feature of the proposed task reconstruction method (TR-method) is that it is based on a local task reconstruction as opposed to the local joint reconstruction of many other approaches. And, this method has dynamic task priority assignment feature which ensures the system stability under singular regions owing to the change of task priority. The TR-method enables us to increase the task controller gain to improve the task performance whereas this increase can destabilize the system for the conventional algorithms in real experiments. In addition, the physical meaning of tuning parameters is very straightforward. Hence, we can maximize task performance even near the singular region while simultaneously obtaining the singularity-free motion. The advantage of the proposed method is experimentally tested by using the 7-dof spatial manipulator, and the result shows that the new method improves the performance several times over the existing algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2102-2107
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• 2005

In this paper, a real-time optimization method for nonlinear dynamical systems is proposed. The proposed method is based on the algorithms of numerical solutions for optimal control problems. We deal with a real-time collision-free motion control of a nonholonomic mobile robot, which has input restrictions of actuators. The effectiveness of the algorithmic method is demonstrated through numerical and experimental results. The mobile robot which we have developed is able to avoid moving obstacles skillfully. Therefore the proposed controller works well in real time.

• The KIPS Transactions:PartA
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• v.11A no.1
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• pp.97-108
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• 2004

This paper proposes to use an algorithmic debugging technique for locating logical errors in Java programs. The algorithmic debugging is a semi-automated debugging technique that builds an execution tree from a source program and locates logical errors, if any, included in the program from the execution tree with top-down method. So, it is very important to build a suitable execution tree from the various programming languages. In this paper we propose the method for building an execution tree iron Java programs and walk through an example. This approach could reduce the number of interactions between a user and a debugging system than the traditional step-wise debugging technique.

• The Transactions of the Korean Institute of Power Electronics
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• v.7 no.1
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• pp.11-17
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• 2002

This paper describes on a position detection method for the motors which have repetitive operations using the Algorithmic State Machine(ASM), one of the digital logic design methods. With analyses for the incremental encoder output patterns, state diagram and state table are constructed and a digital circuit which can detect the changing point of direction of motor rotation is designed. To verify the validity of the designed circuit, simulations for all cases in which the direction of motor rotation is changed, are performed. Simulation results show the designed digital circuit can detect the direction of motor rotation accurately for all cases.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2004.04a
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• pp.107-114
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• 2004

In this work the Model algorithmic control method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first model is then extracted from the neural model. Results of simulations for MAC control of grade change operations are compared with plant operation data response. From the comparison, we can see that the proposed MAC method exhibits faster response for the grade change of paper and achieves stable steady-state.

• Journal of Korean Institute of Industrial Engineers
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• v.38 no.3
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• pp.198-207
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• 2012

In product or process parameter design, the case of multiple performance characteristics appears more commonly than that of a single characteristic. Numerous methods have been developed to deal with such multi-characteristic parameter design (MCPD) problems. Among these, this paper considers three representative methods, which are respectively based on the desirability function (DF), grey relational analysis (GRA), and principal component analysis (PCA). These three methods are then used to solve the MCPD problems in ten case studies reported in the literature. The performance of each method is evaluated for various combinations of its algorithmic parameters and alternatives. Relative performances of the three methods are then compared in terms of the significance of a design parameter and the overall performance value corresponding to the compromise optimal design condition identified by each method. Although no method is significantly inferior to others for the data sets considered, the GRA-based and PCA-based methods perform slightly better than the DF-based method. Besides, for the PCA-based method, the compromise optimal design condition depends much on which alternative is adopted while, for the GRA-based method, it is almost independent of the algorithmic parameter, and therefore, the difficulty involved in selecting an appropriate algorithmic parameter value can be alleviated.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.73-77
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• 2003

This paper presents a high-speed algorithmic analog-to-digital converter (ADC), which is based on gray coding. The realization method makes use of a two-input maximum circuit to provide a high-speed operation and a low-distortion in the transfer characteristic. The proposed ADC based on the CMOS integrated circuit technique is simple and suitable for implementing a highresolution ADC. The performances of the proposed circuit were studied using the PSPICE analog simulation program. The simulation-results verifying the circuit performances are agreed with the expected values.

• 한국정보디스플레이학회:학술대회논문집
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• 2005.07b
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• pp.1171-1174
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• 2005

This thesis provides a new digital convergence method and apparatus which uses area photo sensor and new analog to digital algorithmic to identify the correct digital pattern position factor of the projection system. This method and apparatus can be applied to the convergence correction in a TV receiver with a display that comprises a display screen with photo sensors positioned adjacent to screen edges. It also can be applied to the manufacture process detection of the projection system to adjust the associative convergence parameter. In this paper, demonstrate how this convergence detecting algorithmic was implemented with four area photo sensors of special designed pattern to the rear projection CRT TV.

• Journal of Korea Technical Association of The Pulp and Paper Industry
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• v.36 no.5 s.108
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• pp.11-20
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• 2004

In this work the Model Algorithmic Control (MAC) method is applied to control the grade change operations in paper mills. The neural network model for the grade change operations is identified first and the impulse model is extracted from the neural network model. Results of simulations for MAC control of grade change operations are compared with plant operation data. The major contribution of the present work is the application of MAC in the industrial plants based on the identification of neural network models. We can confirm that the proposed MAC method exhibits faster responses and less oscillatory behavior compared to the plant operation data in the grade change operations.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.3-15
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• 1997

In order to apply fuzzy logic, two major tasks need to be performed: the derivation of fuzzy rules and the determination of membership functions. These tasks are often difficult and time-consuming. This paper presents an algorithmic method for generating membership functions and fuzzy rules applicable to decision-making problems; the method includes an entropy minimization for clustering analog samples. Membership functions are derived by partitioning the variables into desired number of fuzzy terms, and fuzzy rules are obtained using minimum entropy clustering. In the mle derivation process, rule weights are also calculated. Inference and defuzzification for classification problems are also discussed.