• International Journal of Aeronautical and Space Sciences
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• v.8 no.1
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• pp.10-20
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• 2007

For spacecraft attitude control, reaction wheel (RW) steering laws with more than three wheels for three-axis attitude control can be derived by using a control allocation (CA) approach.1-2 The CA technique deals with a problem of distributing a given control demand to available sets of actuators.3-4 There are many references for CA with applications to aerospace systems. For spacecraft, the control torque command for three body-fixed reference frames can be constructed by a combination of multiple wheels, usually four-wheel pyramid sets. Multi-wheel configurations can be exploited to satisfy a body-axis control torque requirement while satisfying objectives such as minimum control energy.1-2 In general, the reaction wheel steering laws determine required torque command for each wheel in the form of matrix pseudo-inverse. In general, the attitude control command is generated in the form of a feedback control. The spacecraft body angular rate measured by gyros is used to estimate angular displacement also.⁵ Combination of the body angular rate and attitude parameters such as quaternion and MRPs(Modified Rodrigues Parameters) is typically used in synthesizing the control command which should be produced by RWs.¹ The attitude sensor signals are usually corrupted by noise; gyros tend to contain errors such as drift and random noise. The attitude determination system can estimate such errors, and provide best true signals for feedback control.⁶ Even if the attitude determination system, for instance, sophisticated algorithm such as the EKF(Extended Kalman Filter) algorithm⁶, can eliminate the errors efficiently, it is quite probable that the control command still contains noise sources. The noise and/or other high frequency components in the control command would cause the wheel speed to change in an undesirable manner. The closed-loop system, governed by the feedback control law, is also directly affected by the noise due to imperfect sensor characteristics. The noise components in the sensor signal should be mitigated so that the control command is isolated from the noise effect. This can be done by adding a filter to the sensor output or preventing rapid change in the control command. Dynamic control allocation(DCA), recently studied by Härkegård, is to distribute the control command in the sense of dynamics⁴: the allocation is made over a certain time interval, not a fixed time instant. The dynamic behavior of the control command is taken into account in the course of distributing the control command. Not only the control command requirement, but also variation of the control command over a sampling interval is included in the performance criterion to be optimized. The result is a control command in the form of a finite difference equation over the given time interval.⁴ It results in a filter dynamics by taking the previous control command into account for the synthesis of current control command. Stability of the proposed dynamic control allocation (CA) approach was proved to ensure the control command is bounded at the steady-state. In this study, we extended the results presented in Ref. 4 by adding a two-step dynamic CA term in deriving the control allocation law. Also, the strict equality constraint, between the virtual and actual control inputs, is relaxed in order to construct control command with a smooth profile. The proposed DCA technique is applied to a spacecraft attitude control problem. The sensor noise and/or irregular signals, which are existent in most of spacecraft attitude sensors, can be handled effectively by the proposed approach.

• Journal of The Korean Astronomical Society
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• v.46 no.3
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• pp.125-131
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• 2013

We present the relation between the genus in cosmology and the Betti numbers for excursion sets of three- and two-dimensional smooth Gaussian random fields, and numerically investigate the Betti numbers as a function of threshold level. Betti numbers are topological invariants of figures that can be used to distinguish topological spaces. In the case of the excursion sets of a three-dimensional field there are three possibly non-zero Betti numbers; ${\beta}_0$ is the number of connected regions, ${\beta}_1$ is the number of circular holes (i.e., complement of solid tori), and ${\beta}_2$ is the number of three-dimensional voids (i.e., complement of three-dimensional excursion regions). Their sum with alternating signs is the genus of the surface of excursion regions. It is found that each Betti number has a dominant contribution to the genus in a specific threshold range. ${\beta}_0$ dominates the high-threshold part of the genus curve measuring the abundance of high density regions (clusters). ${\beta}_1$ dominates the genus near the median thresholds which measures the topology of negatively curved iso-density surfaces, and ${\beta}_2$ corresponds to the low-threshold part measuring the void abundance. We average the Betti number curves (the Betti numbers as a function of the threshold level) over many realizations of Gaussian fields and find that both the amplitude and shape of the Betti number curves depend on the slope of the power spectrum n in such a way that their shape becomes broader and their amplitude drops less steeply than the genus as n decreases. This behaviour contrasts with the fact that the shape of the genus curve is fixed for all Gaussian fields regardless of the power spectrum. Even though the Gaussian Betti number curves should be calculated for each given power spectrum, we propose to use the Betti numbers for better specification of the topology of large scale structures in the universe.

• Economic and Environmental Geology
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• v.41 no.6
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• pp.763-771
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• 2008

Uncertainties are pervasive in engineering geological problems. Therefore, the presence of uncertainties and their significance in analysis and design of slopes have been recognized. Since the uncertainties cannot be taken into account by the conventional deterministic approaches in slope stability analysis, the probabilistic analysis has been considered as the primary tool for representing uncertainties in mathematical models. However, some uncertainties are caused by incomplete information due to lack of information, and those uncertainties cannot be handled appropriately by the probabilistic approach. For those uncertainties, the theory of fuzzy sets is more appropriate. Therefore, in this study, fuzzy reliability analysis has been proposed in order to deal with the uncertainties which cannot be quantified in the probabilistic analysis due to the limited information. For the practical example, a slope is selected in this study and both the probabilistic analysis and the fuzzy reliability analysis have been carried out for planar failure. In the fuzzy reliability analysis, the dip angle and internal friction angle of discontinuity are considered as triangular fuzzy numbers since the random properties of the variables cannot be obtained completely under the conditions of limited information. In the study, the fuzzy reliability index and the probabilities of failure are evaluated from fuzzy arithmetic and compared to those from the probabilistic approach using Monte Carlo simulation and point estimate method. The analysis results show that the fuzzy reliability analysis is more appropriate for the condition that the uncertainties arise due to incomplete information.

• Journal of the Korean Society of Hazard Mitigation
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• v.6 no.1 s.20
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• pp.83-95
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• 2006

This study aims to analysis of suspended-load concentration in related to those data by measuring vertical sediments distribution with rainfall using the ASM (Argus Surface Meter)- IV at the channel reach of a upstream and a downstream in small river. The watershed, small river basin where had taken for experimental study was selected, which is a drainage area lied at Walha in Yunkee-Gun, Chungnam Province. Measured data of suspended-load concentration consists of two groups with 2,145 data during 1hr 11min 30sec and 1,216 data during 40min 32sec for measuring time of 2 second in the study reaches at river, respectively. In order to analyze of the vertical concentration distribution, using the data sets are selected the measuring time 16 sets one of these data by random in the study reaches. As a results, the Rouse number of a measured and a calculated value show that a rang of $0.00129{\sim}0.02394$, averaged value of 0.01129 md, a rang of $0.00118{\sim}0.00822$, averaged value of 0.00436 in upstream reaches, and also a rang of $0.065115{\sim}0.065295$, averaged value of 0.06521, and a rang of $0.057315{\sim}0.059109$, averaged value of 0.05795 in downstream reaches, respectively. These difference show that measured Rouse number compared with downstream reach errors of less than in upstream reach, but between measured and calculated of the Rouse number compared with downstream reach errors of more than in upstream reach, respectively. It seems to will be included one of the occurrence errors of variable estimations when Rouse number of calculated value to be made computed by the fall velocity with a high temperature of water using equation of empirical kinematic viscosity was derived in this study.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.2
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• pp.29-38
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• 2012

The data quality and measurements along consecutive detector stations can vary much even in the same traffic conditions due to variety in detector types, calibration and maintenance effort, field operation periods, minor geometric changes of roads and so on. These faulty situations often create 10% or more of inherent difference in important traffic measurements between two stations even under stable low flow condition. Low detection rates(DR) and high false alarm rates(FAR) therefore sets in among many popular Automatic Incident Detection Algorithms(AIDA). This research is two-folded and aims mainly to develop a new AIDA for uninterrupted flow. For this purpose, a technique which utilizes a Simple Arithmetic Operation(SAO) of traffic variables is introduced. This SAO technique is designed to address the inherent discrepancy of detector data observed successive stations, and to overcome the degradation of AIDA performance. It was found that this new algorithm improves DR as much as 95 percent and above. And mean time to detection(MTTD) is found to be 1 minutes or less. When it comes to FAR, this new approach compared to existing AIDAs reduces FAR up to 31.0 percent. And capability in persistency check of on-going incidents was found excellent as well.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.23-33
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• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• The Journal of Fisheries Business Administration
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• v.21 no.2
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• pp.55-84
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• 1990

The objective of this study is to develop an inventory model for the inventory management of a stocking point which sells processed fisheries products. The study, first of all, sets up fisheries processing companies, food companies, apparel companies, pharmaceutical companies and electronic and electrical companies as a population. Then, a comparative study is empirically applied to obtain the inventory characteristics of final products by industry through a survey of a sample selected by a random sampling procedure. The major inventory characteristics of processed fisheries products obtained from the above analysis can be summarized as follows : 1) The major demand characteristics of processed fisheries products is to have wide seasonal fluctuations because the supply of raw materials (i.e., fisheries products) heavily depends on the productive capacity of nature. 2) It has found that fisheries processing companies are the worst in inventory management among the various industries selected in the sample. However, the self-rating of inventory management system by inventory managers of companies shows that the fisheries processing companies are relatively higher than the other companies. 3) The portion of inventory holding cost out of inventory relevant cost is very high for processed fisheries products compared with final products of the other industries. 4) Processed fisheries products are distributed to final consumers through roughly two distribution echelons and take a parallel type inventory system for their distribution structure. In order to develop an inventory model which reflects the inventory characteristics of processed fisheries products mentioned in the above, an inventory model with partial backorders is developed under the situation of stochastic lead time under the consideration of the inventory characteristics of processed fisheries products and then an iterative solution method is provided for the model. Then this study analyzes sensitivity for the standard deviation of lead time in the model by numerical examples.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.12
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• pp.79-90
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• 2005

In this paper, we propose a new clustered reconfigurable interconnect network (CRIN) BIST to improve the embedding probabilities of random-pattern-resistant-patterns. The proposed method uses a scan-cell reordering technique based on the signal probabilities of given test cubes and specific hardware blocks that increases the embedding probabilities of care bit clustered scan chain test cubes. We have developed a simulated annealing based algorithm that maximizes the embedding probabilities of scan chain test cubes to reorder scan cells, and an iterative algorithm for synthesizing the CRIN hardware. Experimental results demonstrate that the proposed CRIN BIST technique achieves complete fault coverage with lower storage requirement and shorter testing time in comparison with the conventional methods.

• Journal of Astronomy and Space Sciences
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• v.31 no.1
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• pp.1-6
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• 2014

Quite recently, it has been suggested that the interaction of the solar wind with Mercury results in the variation in the solar wind velocity in the Earth's neighborhood during inferior conjunctions with Mercury. This suggestion has important implications both on the plasma physics of the interplanetary space and on the space weather forecast. In this study we have attempted to answer a question of whether the claim is properly tested. We confirm that there are indeed ups and downs in the profile of the solar wind velocity measured at the distance of 1 AU from the Sun. However, the characteristic attribute of the variation in the solar wind velocity during the inferior conjunctions with Mercury is found to be insensitive to the phase of the solar cycles, contrary to an earlier suggestion. We have found that the cases of the superior conjunctions with Mercury and of even randomly chosen data sets rather result in similar features. Cases of Venus are also examined, where it is found that the ups and downs with a period of ~ 10 to 15 days can be also seen. We conclude, therefore, that those variations in the solar wind velocity turn out to be a part of random fluctuations and have nothing to do with the relative position of inner planets. At least, one should conclude that the solar wind velocity is not a proper observable modulated by inner planets at the distance of 1 AU from the Sun in the Earth's neighborhood during inferior conjunctions.

• The Journal of the Korea Contents Association
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• v.20 no.3
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• pp.267-274
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• 2020

Click-through rate (CTR) prediction is an estimate of the probability that a user will click on a given item and plays an important role in determining strategies for maximizing online ad revenue. Recently, research has been performed to utilize CNN for CTR prediction. Since the CTR data does not have a meaningful order in terms of correlation, the CTR data may be arranged in any order. However, because CNN only learns local information limited by filter size, data arrays can have a significant impact on performance. In this paper, we propose a multi-array CNN model that generates a data array set that can extract all local feature information that CNN can collect, and learns features through individual CNN modules. Experimental results for large data sets show that the proposed model achieves a 22.6% synergy with RI in AUC compared to the existing CNN, and the proposed array generation method achieves 3.87% performance improvement over the random generation method.