• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.262-274
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• 2010

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted moving average (DEWMA) control output, by using the EWMA and recursive least squares (RLS) prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are parallel, and six cases are addressed. Within the context of time-varying metrology delay, this paper presents a modified recursive least squares-linear trend (RLS-LT) controller, in combination with runs test. Simulated single input-single output (SISO) R2R processes subject to various time-varying metrology delay scenarios are used as a testbed to evaluate the proposed algorithms. The simulation results indicate that the modified RLS-LT controller can yield the process output more accurately on target with smaller mean squared error (MSE) than the original RLSLT controller that only deals with constant metrology delays.

• Journal of Electrical Engineering and information Science
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• v.1 no.2
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• pp.77-86
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• 1996

In this paper we deal with the problem of recovering 3-D motion and structure from a time-varying 2-D velocity vector field. A great deal has been done on this topic, most of which has concentrated on finding necessary and sufficient conditions for there to be a unique 3-D solution corresponding to a given 2-D motion. While previous work provides useful theoretical insight, in most situations the known algorithms have turned out to be too sensitive to be of much practical use. It appears that any robust algorithm must improve the 3-D solutions over time. As a step toward such algorithm, we present a method for recovering 3-D motion and structure from a given time-varying 2-D velocity vector field. The surface of the object in the scene is assumed to be locally planar. It is also assumed that 3-D velocity vectors are piecewise constant over three consecutive frames (or two snapshots of flow field). Our formulation relates 3-D motion and object geometry with the optical flow vector as well as its spatial and temporal derivatives. The linearization parameters, or equivalently, the first-order flow approximation (in space and time) is sufficient to recover rigid body motion and local surface structure from the local instantaneous flow field. We also demonstrate, through a sensitivity analysis carried out for synthetic and natural motions in space, that 3-D motion can be recovered reliably.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.2
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• pp.247-252
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• 2015

Adaptive algorithms based on the criterion of zero-error probability (ZEP) have robustness to impulsive noise and their decision feedback (DF) versions are known to compensate effectively for severe multipath channel distortions. However the ZEP-DF algorithm computes several summation operations at each iteration time for each filter section and this plays an obstacle role in practical implementation. In this paper, the ZEP-DF with recursive gradient estimation (RGE) method is proposed and shown to reduce the computational burden of O(N) to a constant which is independent of the sample size N. Also the weight update of the initial state and the steady state is a continuous process without bringing about any propagation of gradient estimation error in DF structure.

• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.60-67
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• 1997

CAD systems offer a variety of techniques for designing and rendering models of static 3D objects and even of mechanisms, but relatively few tools exist for interactively specifying arbitrary movements of rigid bodies through space. Such tools are essential, not only for artistic animation, but also, for planning and demonstrating assembly and disassembly procedure of manufactured products. A rigid body motion is a continuous mapping from the time domain to a set of positions. To relieve the designers from the burden of specifying this mapping in abstract mathematical terms, combinations of simple rigid motion primitives, such as linear translations or constant axis rotations, are often used. These simple motions are planar and thus ill-suited for approximating arbitrary motions in 3D-space. Instead, we propose the screw motion primitive, a special combination of linear translations and constant axis rotations, which has a simple geometric representation that can be automatically and unambiguously computed from the starting and ending positions of the moving body. Although, any two positions may be interpolated by an infinity of motions, we chose the screw motion for its relative generality and its computational advantages. The paper covers original algorithms for computing the screw motions from interpolated positions and envelopes of swept regions to predict collisions.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.5
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• pp.679-683
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• 2007

Statistical learning theory by Vapnik consists of support vector machine(SVM), support vector regression(SVR), and support vector clustering(SVC) for classification, regression, and clustering respectively. In this algorithms, SVC is good clustering algorithm using support vectors based on Gaussian kernel function. But, similar to SVM and SVR, SVC needs to determine kernel parameters and regularization constant optimally. In general, the parameters have been determined by the arts of researchers and grid search which is demanded computing time heavily. In this paper, we propose a differential evolution based SVC(DESVC) which combines differential evolution into SVC for efficient selection of kernel parameters and regularization constant. To verify improved performance of our DESVC, we make experiments using the data sets from UCI machine learning repository and simulation.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.2B no.3
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• pp.81-89
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• 2002

The sensorless AC motor drive is a popular topic of study due to the cost and reliability of speed and position sensors. Most sensorless algorithms are based on the mathematical modeling of motors including electrical variables such as phase current and voltage. Therefore, the accuracy of such variables largely affects the performance of the sensorless AC motor drive. However, the output voltage of the SVPWM-VSI, which is widely used in sensorless AC motor drives, has considerable errors. In particular, the SVPWM-VSI is error-prone in the low speed range because the constant DC link voltage causes poor resolution in a low output voltage command and the output voltage is distorted due to dead time and voltage drop. This paper investigates a novel high-performance strategy for overcoming these problems in a sensorless ac motor drive. In this paper, a variation of the DC link voltage and a direct compensation for dead time and voltage drop are proposed. The variable DC link voltage leads to an improved resolution of the inverter output voltage, especially in the motor's low speed range. The direct compensation for dead time and voltage drop directly calculates the duration of the switching voltage vector without the modification of the reference voltage and needs no additional circuits. In addition, the proposed strategy reduces a current ripple, which deteriorates the accuracy of a monitored current and causes torque ripple and additional loss. Simulation and experimentation have been performed to verify the proposed strategy.

• Journal of KIISE:Information Networking
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• v.37 no.6
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• pp.444-452
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• 2010

To avoid a channel zapping delay, the device needs to predict a next channel and must download it. Previous researches suggest algorithms based on assumption by utilizing many data. How to select and download future channels is a main issue in IPTV system. Most proposal based on assumption do not guarantees complete performance of channel zapping. In this paper, we suggest an effective method to avoid a channel zapping time with a constant performance by using a number remote controller key information.

• Journal of Communications and Networks
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• v.12 no.1
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• pp.74-85
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• 2010

For the purpose of compromising hosts, attackers including infected hosts initially perform a portscan using IP addresses in order to find vulnerable hosts. Considerable research related to portscan detection has been done and many algorithms have been proposed and implemented in the network intrusion detection system (NIDS). In order to distinguish portscanners from remote hosts, most portscan detection algorithms use a fixed threshold that is manually managed by the network manager. Because the threshold is a constant, even though the network environment or the characteristics of traffic can change, many false positives and false negatives are generated by NIDS. This reduces the efficiency of NIDS and imposes a high processing burden on a network management system (NMS). In this paper, in order to address this problem, we propose an automatic portscan detection system using an fast increase slow decrease (FISD) scheme, that will automatically and adaptively set the threshold based on statistical data for traffic during prior time periods. In particular, we focus on reducing false positives rather than false negatives, while the threshold is adaptively set within a range between minimum and maximum values. We also propose a new portscan detection algorithm, rate of increase in the number of failed connection request (RINF), which is much more suitable for our system and shows better performance than other existing algorithms. In terms of the implementation, we compare our scheme with other two simple threshold estimation methods for an adaptive threshold setting scheme. Also, we compare our detection algorithm with other three existing approaches for portscan detection using a real traffic trace. In summary, we show that FISD results in less false positives than other schemes and RINF can fast and accurately detect portscanners. We also show that the proposed system, including our scheme and algorithm, provides good performance in terms of the rate of false positives.

• The KIPS Transactions:PartA
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• v.10A no.3
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• pp.181-188
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• 2003

We propose a heuristic on-line scheduling algorithm for the IRIS (Increasing Reward with Increasing Service) tasks, which has low computation complexity and produces total reward approximated to that of previous on-line optimal algorithms. The previous on-line optimal algorithms for IRIS tasks perform scheduling on all tasks in a system to maximize total reward. Therefore, the complexities of these algorithms are too high to apply them to practical systems handling many tasks. The proposed algorithm doesn´t perform scheduling on all tasks in a system, but on (constant) W´s tasks selected by a predefined task selection policy. The proposed algorithm is based on task selection policies that define how to select tasks to be scheduled. We suggest two simple and intuitive selection policies and a generalized selection policy that integrates previous two selection policies. By narrowing down scheduling scope to only W´s selected tasks, the computation complexity of proposed algorithm can be reduced to O(Wn). However, simulation results for various cases show that it is closed to O(W) on the average.

• ETRI Journal
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• v.21 no.2
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• pp.22-30
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• 1999

In this paper, we derive an efficient parallel algorithm to solve the single function coarsest partition problem. This algorithm runs in O(\log2n) time using O(nlogn) operations on the EREW PRAM with O(n) memory cells used. Compared with the previous PRAM algorithms that consume O(n1+${\varepsilon}$) memory cells for some positive constant ${\varepsilon}\>0$, our algorithm consumes less memory cells without increasing the total number of operations.