• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.688-695
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• 2017

This paper deals with characteristics analysis of a permanent magnet (PM) linear generator using analytical methods for wave energy harvesting. The wave energy is carried out from the movement of a yo-yo system. A linear generator using permanent magnets to generate a magnetic force itself does not require a separate power supply and has the advantage of simple maintenance. In addition to the use of a rare earth, a permanent magnet having a high-energy density can be miniaturized and lightweight, and can obtain high energy-conversion efficiency. We derived magnetic field solutions produced by the permanent magnet and armature reaction based on 2D polar coordinates and magnetic vector potential. Induced voltage is obtained via arbitrary sinusoidal input. In addition, electrical parameters are obtained, such as back-EMF constant, resistance, and self- and mutual-winding inductances. The space harmonic method used in this paper is confirmed by comparing it with finite element method (FEM) results. These facilitate the characterization of the PM-type linear generator and provide a basis for comparative studies, design optimization, and machine dynamic modeling.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.179-188
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• 2008

An important issue in multiagent reinforcement learning is how an agent should team its optimal policy through trial-and-error interactions in a dynamic environment where there exist other agents able to influence its own performance. Most previous works for multiagent reinforcement teaming tend to apply single-agent reinforcement learning techniques without any extensions or are based upon some unrealistic assumptions even though they build and use explicit models of other agents. In this paper, basic concepts that constitute the common foundation of multiagent reinforcement learning techniques are first formulated, and then, based on these concepts, previous works are compared in terms of characteristics and limitations. After that, a policy model of the opponent agent and a new multiagent reinforcement learning method using this model are introduced. Unlike previous works, the proposed multiagent reinforcement learning method utilize a policy model instead of the Q function model of the opponent agent. Moreover, this learning method can improve learning efficiency by using a simpler one than other richer but time-consuming policy models such as Finite State Machines(FSM) and Markov chains. In this paper. the Cat and Mouse game is introduced as an adversarial multiagent environment. And effectiveness of the proposed multiagent reinforcement learning method is analyzed through experiments using this game as testbed.

• Journal of Wetlands Research
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• v.19 no.4
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• pp.375-382
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• 2017

In this study, general characteristics of dynamic behavior of stormwater runoff from a small impervious catchment was investigated from a series of simulations for a radial type surface runoff. Based on the simulation results, a better placement strategy for stormwater inlets to improve performance of a structural best management practice (BMP) was suggested. The degree of pollutant first flush from an ideal radial type impervious catchment was simulated using a 1-D diffusion wave equation coupled with a pollutant transport equation. The results showed that the first flush of the chemical oxygen demand was the strongest when the catchment length ranged 30-50m at a bed slope of 0.02. This result suggested that a required degree of the first flush can be intentionally obtained by just changing the locations and numbers of stormwater inlets, and thereby adjusting the catchment length. Particularly, the overall performance of a structural BMP in reducing pollutant load can be improved by placing the stormwater inlets at locations for obtaining a required first flush strength.

• Journal of Korea Water Resources Association
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• v.47 no.8
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• pp.671-684
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• 2014

Change point analysis is a efficient tool to understand the fundamental information in hydro-meteorological data such as rainfall, discharge, temperature etc. Especially, this fundamental information to change points to future rainfall data identified by reasonable detection skills can affect the prediction of flood and drought occurrence because well detected change points provide a key to resolve the non-stationary or inhomogeneous problem by climate change. Therefore, in this study, the comparative study to assess the performance of the 3 change point detection skills, cumulative sum (CUSUM) method, Bayesian change point (BCP) method, and segmentation by dynamic programming (DP) was performed. After assessment of the performance of the proposed detection skills using the 3 types of the synthetic series, the 2 reasonable detection skills were applied to the observed and future rainfall data at the 5 rainfall gauges in South Korea. Finally, it was suggested that BCP (with 0.9 posterior probability) could be best detection skill and DP could be reasonably recommended through the comparative study. Also it was suggested that BCP (with 0.9 posterior probability) and DP detection skills to find some change points could be reasonable at the North-eastern part in South Korea. In future, the results in this study can be efficiently used to resolve the non-stationary problems in hydrological modeling considering inhomogeneity or nonstationarity.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.2 no.2
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• pp.108-112
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• 2016

Molecular imaging with the radiolabeled RGD peptides for ${\alpha}_v{\beta}_3$ integrin has been an increasing interest for tumor diagnosis and the treatment monitoring. Recently, $^{64}Cu$-NODAGA-gluco-E[c(RGDfK)]$_2$ was developed for quantification of ${\alpha}_v{\beta}_3$ integrin and its biological properties was elucidated. To better understand the molecular process in vivo, we performed the kinetic analysis for the $^{64}Cu$-NODAGA-gluco-E[c(RGDfK)]$_2$. After preparation of a radiotracer, dynamic PET images were obtained in the U87MG xenograft mice for 60 min (n = 6). Binding potential values were estimated from the 3-tissue compartment model, reference Logan and simplified reference tissue model. In the early time frame (0-20 min), the liver, kidney, intestine, urinary bladder and tumor were visualized but these uptakes were diminished as time went by. The tumors showed a good contrast at 40 min after administration. $^{64}Cu$-NODAGA-gluco-E[c(RGDfK)]$_2$ showed the 2-fold uptake in the tumor compared with that in the muscle. The parametric maps for binding values also provide the higher tumor-to-background contrast than the static images. A binding value obtained from the 3-tissue compartment model was comparable to other modeling methods. From these results, we conclude that $^{64}Cu$-NODAGA-gluco-E[c(RGDfK)]$_2$ may be a promising PET radiotracer for the evaluation of angiogenesis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.3
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• pp.461-467
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• 2003

This paper proposes the method of mobility implementation for location searching in the intelligence wireless ATM networks that expand and apply standard broadband signaling capabilities, and analyze the performance based on the numerical algorithm. The existing B-ISDN UNI protocol stack demands the location search mechanism to determine the location of mobile terminal in the wireless ATM networks because it use single protocol through the fixed PTP interface or PTM interface that don't support terminal mobility. The proposed method make possible the dynamic mobility at a part of wireless access by minimizing the signaling load without a falling-off in system performance by using the intelligence network technology according to the expansion of ATM and B-ISDN signaling integration based on the fixed networks. We implemented the performance analysis by MFC modeling based on numerical algorithm, and realized the efficiency of expenses by carrying out the comparative signaling performance evaluation to measure the relative gains of location search service in the intelligence wireless ATM system. The obtained results have the flexibility to operate in the public B-ISDN network environment without a change of existing B-ISDN/ATM NNI signaling reference to support the wireless ATM access system, and can easily expand to correspond to terminal mobility and various multimedia services in the next broadband PCS.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Journal of the Korea Society of Computer and Information
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• v.19 no.1
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• pp.191-201
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• 2014

The purpose of this study is to propose strategies of puzzle-based learning for Informatics gifted education through analyzing Informatics gifted elementary students' computational problem solving approaches in puzzle-based learning contexts. Six types of educational puzzles, which are constraints, optimization, probability, statistically speaking, pattern recognition, and strategy, were used in teaching 14 Informatics gifted students for 8 sessions. The results of pre and post test and each students' answers were analyzed to identify why students were not able to solve the puzzles. We also analysed what essential computational strategies are needed to solve each type of puzzles, and what students did not know in solving puzzle problems. We identified some problems caused by puzzle representation methods, and various students' intuitions that disturb puzzle solving. Also, we identified essential computational strategies to solve puzzles: backtracking, dynamic programming, abstraction, modeling, and reduction of big problem. However, students had difficulties in applying these strategies to solve their puzzle problems. We proposed the revised puzzle-based learning strategies, which is based on the improved problem representation, just-in-time cognitive feedbacks, and web-based learning system.

• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.183-183
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• 2017

In the early part of rice growth, root volume primarily limits the amount of plant-accessible nitrogen (N). Therefore, knowledge of the root development is important for modeling N uptake of rice. The timing when the volume of rhizosphere cover the whole soil is also important to carry out timely top dressing. However, information about initial root expansion and associated N uptake is limited due to intrinsic technical difficulties in assessing below-ground processes. Some studies, however, showed a close relationship between below-ground root and above-ground leaf development, suggesting a possibility that above-ground attributes could serve as surrogates for the root processes. In this study, we investigated the relationship between below-ground and above-ground development of rice. Field experiments were conducted where we cultivated Koshihikari (a leading cultivar in Japan) for four different cropping schedules in 2012. In 2016, Gimbozu (HEG4) and three flowering time mutant lines of Gimbozu (X61 (se13), HS276 (ef7), DMG9 (se13, ef7)) were examined for a single season. Experiments were performed with three replications in a completely randomized design. We monitored ammonium-N concentration ([NH4+-N]) in soil solution by repeatedly taking samples from a porous tubing (10-cm long) vertically inserted at the most distant point from surrounding rice hills. Samples were taken in triplicate (= triplicate tubes) and every three days from transplanting in each experimental unit. For above-ground attributes, leaf area index (LAI) was measured in 2012, whereas soil coverage ratio was estimated by image processing in 2016. Results showed that [NH4+-N] increased gradually after transplanting and then rapidly decreased from a certain day. This distinct drop in [NH4+-N] informed us the timing at which the rice root system reached the point of porous tubing and thus essentially covered the whole soil volume. The LAI at the dropping point was about 0.43 regardless of the cropping schedules in 2012 experiment. In 2016, the coverage ratio at the N dropping point was within the range of 0.12 to 0.19 for four genotypes having different growth durations. In addition, the coverage ratios at seven weeks after the transplanting showed a good correspondence to LAI across the four genotypes. We therefore conclude that both LAI and coverage ratio may serve as robust indicators for root development and might be useful to estimate the timing when the root system fully cover the soil volume. Results obtained here will also contribute to develop models that can predict not only above-ground canopy development but also associated below-ground processes.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.6
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• pp.39-47
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• 1998

A fourth-order $\Sigma$-$\Delta$ modulator is designed and implemented in 0.6 $\mu\textrm{m}$ CMOS technology. The modulator is verified by introducing nonlinear factors such as DC gain and slew rate in system model that determines the transfer function in S-domain and in time-domain. Dynamic range is more than 110 dB and the peak SM is 102.6 dB at a clock rate of 2.8224 MHz for voiceband signal. The structure of a ∑-$\Delta$ modulator is a modified fourth-order ∑-$\Delta$ modulator using direct feedback loop method, which improves performance and consumes less power. The transmission zero for noise is located in the first-second integrator loop, which reduces entire size of capacitors, reduces the active area of the chip, improves the performance, and reduces power dissipation. The system is stable because the output variation with respect to unit time is small compared with that of the third integrator. It is easy to implement because the size of the capacitor in the first integrator, and the size of the third integrator is small because we use the noise reduction technique. This paper represents a new design method by modeling that conceptually decides transfer function in S-domain and in Z-domain, determines the cutoff frequency of signal, maximizes signal power in each integrator, and decides optimal transmission-zero frequency for noise. The active area of the prototype chip is 5.25$\textrm{mm}^2$, and it dissipates 10 mW of power from a 5V supply.