• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.04a
• /
• pp.613-616
• /
• 2000

This paper considers the problem of subway crew scheduling. Crew scheduling is concerned with finding a minimum number of assignments of crews to a given timetable satisfying various restrictions. Traditionally, crew scheduling problem has been formulated as a set covering or set partitioning problem possessing exponentially many variables, but even the LP relaxation of the problem is hard to solve due to the exponential number of variables. In this paper, we propose two basic techniques that solve the problem in a reasonable time, though the optimality of the solution is not guaranteed. To reduce the number of variables, we adopt column-generation technique. We could develop an algorithm that solves column-generation problem in polynomial time. In addition, the integrality of the solution is accomplished by variable-fixing technique. Computational results show column-generation makes the problem of treatable size, and variable fixing enables us to solve LP relaxation in shorter time without a considerable increase in the optimal value. Finally, we were able to obtain an integer optimal solution of a real instance within a reasonable time.

• Journal of Korea Multimedia Society
• /
• v.4 no.5
• /
• pp.465-475
• /
• 2001

Recent1y, there is growing attention on the researches of inducing association rules from large volume of database. One of them is the method that can be applied to quantitative attribute data. This paper presents a new method for generating large-interval itemsets, which uses locality for partitioning the range of data. This method can minimize the loss of data-inherent characteristics by generating denser large-interval items than other methods. Performance evaluation results show that our new approach is more efficient than previously proposed techniques.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.4
• /
• pp.456-465
• /
• 2006

The problem of recursive filtering far linear discrete-time systems with uncertainties is considered. A new suboptimal filtering algorithm is herein proposed. It is based on the fusion formula, which represents an optimal mean-square linear combination of local Kalman estimates with weights depending on cross-covariances between local filtering errors. In contrast to the optimal weights, the suboptimal weights do not depend on current measurements, and thus the proposed algorithm can easily be implemented in real-time. High accuracy and efficiency of the suboptimal filtering algorithm are demonstrated on the following examples: damper harmonic oscillator motion and vehicle motion constrained to a plane.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.33A no.7
• /
• pp.261-268
• /
• 1996

Hardware-software codesign becomes improtant to effectively sagisfy perfomrance goals, because designers can trade-off in the way hardware and software components work teogether to exhibit a specified behavior. In this paper, a hardware-software pratitioning algorithm is presetned, in which the system behavioral description containing a mixture of hardware and software components is partitioned into hardware part and software part. The partitioning algorithm tries to minimize the given cost function under constraints on hardware resources or latency. Recursive moving of operations between the hardware and software parts is used to find a near optimum partition and the list scheduling approach is used to estimate the hardware area and latency. Since memory may take substantial protion of the hardware part, memory cost is included in sthe hardware cost. Experimental resutls show that our algorithm is effective.

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

• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.667-672
• /
• 2001

An optimization approach is used to partition the field of view. A cost function is defined to represent the constraints on the solution, which is then mapped onto a two-dimensional Hopfield neural network for minimization. Each neuron in the network represents a possible match between a field of view and one or multiple objects. Partition is achieved by initializing each neuron that represents a possible match and then allowing the network to settle down into a stable state. The network uses the initial inputs and the compatibility measures between a field of view and one or multiple objects to find a stable state.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.27 no.4
• /
• pp.67-86
• /
• 2002

This paper considers subway crew scheduling problem. Crew scheduling is concerned with finding a minimum number of assignments of crews to a given timetable satisfying various restrictions. Traditionally, crew scheduling problem has been formulated as a set covering or set partitioning problem possessing exponentially many variables, but even the LP relaxation of the problem is hard to solve due to the exponential number of variables. In this paper. we propose two basic techniques that solve the subway crew scheduling problem in a reasonable time, though the optimality of the solution is not guaranteed. We develop an algorithm that solves the column-generation problem in polynomial time. In addition, the integrality of the solution is accomplished by variable-fixing technique. Computational result for a real instance is reported.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.12 no.3
• /
• pp.254-266
• /
• 1987

The concept of diakoptics is to analyze a large scale network by partitioning it in to a number of smaller subnetworks. The theory has been developed from the concepts of open path and closed path through the conventional graph theoretic approach. In this paper, the formulation of characteristic equation of the eigenvalues of the network is represented by the aplication of diakoptics to the simulated netwrok model of any linear large scale network. Furtheromore, diakoptics coupled with appropriately proposed algorithm for the interative solution of the characteristic equation results in considerable computational efficiency as compare with nondiakoptical mothods.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.488-494
• /
• 2001

Many literatures, so far, have concentrated on approaches employing dependent coordinates set resulting in computational burden of constraint forces, which is needless in many cases. Some researchers developed methods to remove or calculate it efficiently. But systematic generation of the motion equation using independent coordinates set by Kane's equation is possible for any closed loop system. Independent velocity transformation method builds the smallest size of motion equation, but needs practically more complicated code implementation. In this study, dependent velocity matrix is systematically transformed into independent one using dependent-independent transformation matrix of each body group, and then motion equation free of constraint force is constructed. This method is compared with the other approach by counting the number of multiplications for car model with 15 d.o.f..

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2002.04a
• /
• pp.274-283
• /
• 2002

This paper presents an efficient models for reinforeced concrete structures using CART-ANFIS(classification and regression tree-adaptive neuro fuzzy inference system). a fuzzy decision tree parttitions the input space of a data set into mutually exclusive regions, each of which is assigned a label, a value, or an action to characterize its data points. Fuzzy decision trees used for classification problems are often called fuzzy classification trees, and each terminal node contains a label that indicates the predicted class of a given feature vector. In the same vein, decision trees used for regression problems are often called fuzzy regression trees, and the terminal node labels may be constants or equations that specify the Predicted output value of a given input vector. Note that CART can select relevant inputs and do tree partitioning of the input space, while ANFIS refines the regression and makes it everywhere continuous and smooth. Thus it can be seen that CART and ANFIS are complementary and their combination constitutes a solid approach to fuzzy modeling.