• 한국CDE학회논문집
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• 제15권1호
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• pp.24-32
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• 2010

This paper introduces a B-spline based branch & bound algorithm for global optimization. The branch & bound is a well-known algorithm paradigm for global optimization, of which key components are the subdivision scheme and the bound calculation scheme. For this, we consider the B-spline hypervolume to approximate an objective function defined in a design space. This model enables us to subdivide the design space, and to compute the upper & lower bound of each subspace where the bound calculation is based on the LHS sampling points. We also describe a search tree to represent the searching process for optimal solution, and explain iteration steps and some conditions necessary to carry out the algorithm. Finally, the performance of the proposed algorithm is examined on some test problems which would cover most difficulties faced in global optimization area. It shows that the proposed algorithm is complete algorithm not using heuristics, provides an approximate global solution within prescribed tolerances, and has the good possibility for large scale NP-hard optimization.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.708-710
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• 2004

This paper studies a multirate sampled-data control for LTI systems by using the digital redesign (DR) method. In this note, to well tackle the problem associated with both the state matching and the stabilization, our nobel strategy is to minimize the linear quadratic cost function. The main features of the proposed method are that i) the delta-operator-based descretization method is applied to improve the state-matching performance in the fast sampling limit and/or the large input multiplicity; ii) the proposed multirate control scheme can improve the state-matching performance in the long sampling limit; iii) some sufficient conditions that guarantee the stability of the closed-loop discrete-time system and provide a guarantee cost for the cost function can be formulated in the LMIs format; and iv) an optimal sampled-data controller in the sense of minimizing the upper bound of the cost function is also given by means of an LMI optimization procedure.

• Journal of Communications and Networks
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• 제14권2호
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• pp.188-194
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• 2012

Cognitive networks (CNs) are capable of enabling dynamic spectrum allocation, and thus constitute a promising technology for future wireless communication. Whereas, the implementation of CN will lead to the requirement of an increased energy-arrival rate, which is a significant parameter in energy harvesting design of a cognitive user (CU) device. A well-designed spectrum-sensing scheme will lower the energy-arrival rate that is required and enable CNs to self-sustain, which will also help alleviate global warming. In this paper, spectrum sensing in a multi-user cognitive ad hoc network with a wide-band spectrum is considered. Based on the prospective spectrum sensing, we classify CN operation into two modes: Distributed and centralized. In a distributed network, each CU conducts spectrum sensing for its own data transmission, while in a centralized network, there is only one cognitive cluster header which performs spectrum sensing and broadcasts its sensing results to other CUs. Thus, a wide-band spectrum that is divided into multiple sub-channels can be sensed simultaneously in a distributed manner or sequentially in a centralized manner. We consider the energy consumption for spectrum sensing only of an analog-to-digital convertor (ADC). By formulating energy consumption for spectrum sensing in terms of the sub-channel sampling rate and whole-band sensing time, the sampling rate and whole-band sensing time that are optimal for minimizing the total energy consumption within sensing reliability constraints are obtained. A power dissipation model of an ADC, which plays an important role in formulating the energy efficiency problem, is presented. Using AD9051 as an ADC example, our numerical results show that the optimal sensing parameters will achieve a reduction in the energy-arrival rate of up to 97.7% and 50% in a distributed and a centralized network, respectively, when comparing the optimal and worst-case energy consumption for given system settings.

• Journal of Advanced Marine Engineering and Technology
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• 제38권10호
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• pp.1303-1309
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• 2014

In this paper, we present an iterative learning control (ILC) framework for tracking problems with predefined data points that are desired points at certain time instants. To design ILC systems for such problems, a new ILC scheme is proposed to produce output curves that pass close to the desired points. Unlike traditional ILC approaches, an algorithm will be developed in which the control signals are generated by solving an optimal ILC problem with respect to the desired sampling points. In another word, it is a direct approach for the multiple points tracking ILC control problem where we do not need to divide the tracking problem into two steps separately as trajectory planning and ILC controller.The strength of the proposed formulation is the methodology to obtain a control signal through learning law only considering the given data points and dynamic system, instead of following the direction of tracking a prior identified trajectory. The key advantage of the proposed approach is to significantly reduce the computational cost. Finally, simulation results will be introduced to confirm the effectiveness of proposed scheme.

• Journal of Power Electronics
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• 제4권2호
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• pp.102-111
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• 2004

This paper is concerned with a deadbeat current control implementation of shunt-type three-phase active power filter (APF). Although the one-dimensional deadbeat control method can attain time-optimal response of APF compensating current, one sampling period is actually required fur its settling time. This delay is a serious drawback for this control technique. To cancel such a delay and one more delay caused by DSP execution time, the desired APF compensating current has to be predicted two sampling periods ahead. Therefore an adaptive predictor is adopted for the purpose of both predicting the control error of two sampling periods ahead and bringing the robustness to the deadbeat current control system. By adding the adaptive predictor output as an adjustment term to the reference value of half a source voltage period before, settling time is made short in a transient state. On the other hand, in a steady state, THD (total harmonic distortion) of the utility grid side AC source current can be reduced as much as possible, compared to the case that ideal identification of controlled system could be made.

• 한국수자원학회논문집
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• 제30권5호
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• pp.441-447
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• 1997

강우계측망이나 인공위성을 이용하여 어떤 지역의 강우를 관측할 경우 각 관측방법의 특성에 따라 관측오차가 발생하게 된다. 즉, 강우계측망을 이용할 경우 관측된 강우는 시간적으로 연속이지만 공간적으로는 불연속의 특성을 갖게 되고 인공위성을 이용하는 경우 강우는 공간적으로는 연속이지만 시간적으로 불연속인 특성을 나타내게 된다. 이에 따라 관측오차의 계산은 강우의 시간적-공간적 통rP특성과 관측방법에 따라 각각 다르게 정량화된다. 현재 이 문제와 관련하여 North와 Nakamoto(1989)가 제안한 관측오차의 추정식이 가장 대표적이라고 할 수 있으며, 이 식에서는 강우의 통계특성과 관측방법에 따른 특성이 고려되어 있다. 이 식은 강우의 특성이 어느 정도 파악되는 경우 적절한 강우관측 계획 수립을 가능할 수 있게 해준다. 본 논문에서는 먼저 관측방법에 따른 관측오차의 계산에 대해 살펴보고, 명 책의 다차원 강우모형을 이용하여 이 문제에 적용해 보았다. 적용 결과 현재까지의 관측오차 계산에는 강우의 2차원 통계 특성만이 고려되기 때문에 모형의 매개변수들이 이 특성에 맞추어 적절히 추정된 경우, 모형에 따른 관측오차의 크기는 크지 않은 것으로 밝혀졌다. 앞으로 이러한 단점은 2차원 이상의 통계특성을 고려하는 관측오차의 추정식, 궁극적으로는 강우의 확률밀도함수를 고려할 수 있는 관측오차의 추정식의 개발을 통해 개선될 수 있을 것이다.

• 한국공작기계학회논문집
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• 제11권6호
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• pp.31-37
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• 2002

This paper presents a new motion control strategy for singularity avoidance in 6 DOF articulated robot manipulators, based on a speed limiting algorithm for joint positions and velocities. For a given task, the robot is controlled so that the joints move with acceptable velocities and positions within the reachable range of each joint by considering the velocity limit. This paper aims at the development of a new efficient method to control robot motion near and at singularities. The proposed method has focused on generating the optimal joint trajectory for a Cartesian end-effector path within the speed limit of each joint by using the speed limit avoidance as well as the acceleration/deceleration scheme. The proposed method was verified using MATLAB-based simulations.

• ETRI Journal
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• 제42권6호
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• pp.932-942
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• 2020

To recover the neural activity from Magnetoencephalography (MEG) and Electroencephalography (EEG) measurements, we need to solve the inverse problem by utilizing the relation between dipole sources and the data generated by dipolar sources. In this study, we propose a new approach based on the implementation of a particle filter (PF) that uses minimum sampling variance resampling methodology to track the neural dipole sources of cerebral activity. We use this approach for the EEG data and demonstrate that it can naturally estimate the sources more precisely than the traditional systematic resampling scheme in PFs.

• Ocean and Polar Research
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• 제35권4호
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• pp.395-405
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• 2013

The aim of this study is to suggest an optimal survey method for coastal habitat monitoring around Weno Island in Chuuk Atoll, Federated States of Micronesia (FSM). This study was carried out to compare and analyze differences between in situ survey (PHOTS) and high spatial satellite imagery (Worldview-2) with regard to the coastal habitat distribution patterns of Weno Island. The in situ field data showed the following coverage of habitat types: sand 42.4%, seagrass 26.1%, algae 14.9%, rubble 8.9%, hard coral 3.5%, soft coral 2.6%, dead coral 1.5%, others 0.1%. The satellite imagery showed the following coverage of habitat types: sand 26.5%, seagrass 23.3%, sand + seagrass 12.3%, coral 18.1%, rubble 19.0%, rock 0.8% (Accuracy 65.2%). According to the visual interpretation of the habitat map by in situ survey, seagrass, sand, coral and rubble distribution were misaligned compared with the satellite imagery. While, the satellite imagery appear to be a plausible results to identify habitat types, it could not classify habitat types under one pixel in images, which in turn overestimated coral and rubble coverage, underestimated algae and sand. The differences appear to arise primarily because of habitat classification scheme, sampling scale and remote sensing reflectance. The implication of these results is that satellite imagery analysis needs to incorporate in situ survey data to accurately identify habitat. We suggest that satellite imagery must correspond with in situ survey in habitat classification and sampling scale. Subsequently habitat sub-segmentation based on the in situ survey data should be applied to satellite imagery.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1648-1672
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• 2020

There are several types of image registration in the sense of stitching separated images that overlap each other. One of these is feature-based registration by a common feature descriptor. In this study, we generate a mosaic of images using feature-based registration for drone aerial images. As a feature descriptor, we apply the scale-invariant feature transform descriptor. In order to investigate the authenticity of the feature points and to have the mapping function, we employ the sample consensus method; we consider the sensed image's inherent characteristic such as the geometric congruence between the feature points of the images to propose a novel hypothesis estimation of the mapping function of the stitching via some optimally chosen initial candidate inliers in the sample consensus method. Based on the experimental results, we show the efficiency of the proposed method compared with benchmark methodologies of random sampling consensus method (RANSAC); the well-ordering property defined in the context and the extensive stitching examples have supported the utility. Moreover, the sample consensus scheme proposed in this study is uncomplicated and robust, and some fatal miss stitching by RANSAC is remarkably reduced in the measure of the pixel difference.