• Journal of Korean Society of Transportation
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• v.24 no.3 s.89
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• pp.17-27
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• 2006

Car following model is a theory that examines changes of condition and interrelationship of acceleration deceleration. headway, velocity and so on closely based on the hypothesis that the Posterior vehicle always follows the preceding vehicle. Car following mode) which is one of the research fields of microscopic traffic flow was first introduced in 1950s and was in active progress in 1960s. However, due to the limitation of data gathering the research depression was prominent for quite a while and then soon was able to tune back on track with development in global positioning system using satellite and generalization of computer use. Recently, there has been many research studies using reception materials of global Positioning system(GPS). Introducing GPS technology to traffic has made real time tracking of a vehicle position possible. Position information is sequential in terms of time and simultaneous measurement of several vehicles in continuous driving is also practicable. Above research was focused on judging whether it is feasible to overcome the following model research by adopting the GPS reception device that was restrictively proceeded due to the limitation of data gathering. For practical judgment, we measured the accuracy and confidence level of the GPS reception devices material by carrying out a practical experiment. Car following model is also being applied in simulations of traffic flow analysis, but due to the difficulty of estimating parameters the basis of the above result. it is our goal to produce an accurate calibration of car following model's parameters that is suitable in this domestic actuality.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.9-16
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• 2016

Wireless sensor networks (WSN) are composed of sensor nodes and a base station. The sensor nodes deploy a non-accessible area, receive critical information, and transmit it to the base station. The information received is applied to real-time monitoring, distribution, medical service, etc.. Recently, the WSN was extended to mobile wireless sensor networks (MWSN). The MWSN has been applied to wild animal tracking, marine ecology, etc.. The important issues are mobility and energy consumption in MWSN. Because of the limited energy of the sensor nodes, the energy consumption for data transmission affects the lifetime of the network. Therefore, efficient data transmission from the sensor nodes to the base station is necessary for sensing data. This paper, proposes an energy consumption model using two-tier clustering in mobile sensor networks (TTCM). This method divides the entire network into two layers. The mobility problem was considered, whole energy consumption was decreased and clustering methods of recent researches were analyzed for the proposed energy consumption model. Through analysis and simulation, the proposed TTCM was found to be better than the previous clustering method in mobile sensor networks at point of the network energy efficiency.

• Journal of Korean Society of Forest Science
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• v.89 no.1
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• pp.49-54
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• 2000

Stand level survival prediction model was developed that incorporated the incidence of fusiform rust(Cronartium quercuum [Berk.] Miyabe ex Shirai f. sp. fusiforme) and allowed the transition of trees from an uninfected stage to an infected stage. The influence of hardwood interspecific competition on the survival of unthinned planted stands of loblolly pine (Pinus taeda L.) was analyzed by using of information from twelve years of tracking a set of permanent plots representing a broad range of plantation parameters. Significant interaction effects between site index and hardwood basal area per acre were revealed in the survival model. Survival of the planted pines decreased with increasing density of hardwood trees per acre and site index as the productivity rating of the forest land. The effects of hardwood trees interspecific competition on loblolly pine tended to show a negative effect on predicted future number of planted pine trees.

• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.510-520
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• 2024

Objective : Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases, characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta. The treatment of PD aims to alleviate motor symptoms by replacing the reduced endogenous dopamine. Currently, there are no disease-modifying agents for the treatment of PD. Zebrafish (Danio rerio) have emerged as an effective tool for new drug discovery and screening in the age of translational research. The neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) is known to cause a similar loss of dopaminergic neurons in the human midbrain, with corresponding Parkinsonian symptoms. L-type calcium channels (LTCCs) have been implicated in the generation of mitochondrial oxidative stress, which underlies the pathogenesis of PD. Therefore, we investigated the neuro-restorative effect of LTCC inhibition in an MPTP-induced zebrafish PD model and suggested a possible drug candidate that might modify the progression of PD. Methods : All experiments were conducted using a line of transgenic zebrafish, Tg(dat:EGFP), in which green fluorescent protein (GFP) is expressed in dopaminergic neurons. The experimental groups were exposed to 500 μmol MPTP from 1 to 3 days post fertilization (dpf). The drug candidates : levodopa 1 mmol, nifedipine 10 μmol, nimodipine 3.5 μmol, diethylstilbestrol 0.3 μmol, luteolin 100 μmol, and calcitriol 0.25 μmol were exposed from 3 to 5 dpf. Locomotor activity was assessed by automated tracking and dopaminergic neurons were visualized in vivo by confocal microscopy. Results : Levodopa, nimodipine, diethylstilbestrol, and calcitriol had significant positive effects on the restoration of motor behavior, which was damaged by MPTP. Nimodipine and calcitriol have significant positive effects on the restoration of dopaminergic neurons, which were reduced by MPTP. Through locomotor analysis and dopaminergic neuron quantification, we identified the neuro-restorative effects of nimodipine and calcitriol in zebrafish MPTP-induced PD model. Conclusion : The present study identified the neuro-restorative effects of nimodipine and calcitriol in an MPTP-induced zebrafish model of PD. They restored dopaminergic neurons which were damaged due to the effects of MPTP and normalized the locomotor activity. LTCCs have potential pathological roles in neurodevelopmental and neurodegenerative disorders. Zebrafish are highly amenable to high-throughput drug screening and might, therefore, be a useful tool to work towards the identification of disease-modifying treatment for PD. Further studies including zebrafish genetic models to elucidate the mechanism of action of the disease-modifying candidate by investigating Ca2+ influx and mitochondrial function in dopaminergic neurons, are needed to reveal the pathogenesis of PD and develop disease-modifying treatments for PD.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.3
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• pp.233-240
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• 2018

Radar cross section (RCS) analysis of chaff clouds is essential for the accurate detection and tracking of missile targets using radar. For this purpose, we compare the performance of two existing methods of predicting RCS of chaff clouds. One method involves summing up the RCS values of individual chaffs in a cloud, while the other method predicts the RCS values using aerodynamic models based on the probability density function. In order to compare and analyze the two techniques more precisely, the RCS of a single chaff computer-aided design model consisting of a half wavelength dipole was calculated using the commercial electromagnetic numerical analysis software, FEKO 7.0, to estimate the RCS values of chaff clouds via simulation. Thus, we verified that our method using the probability density distribution model is capable of analyzing the RCS of chaff clouds more efficiently.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.584-593
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• 2018

The highly efficient operation of induction motors has been studied in the past years. Among the many attempts made to obtain highly efficient operation, Maximum Torque Per Amp (MTPA) controls in induction motor drives were proposed. This method enables induction motor drives to operate very efficiently since it achieves the desired torque with the minimal stator current. This is because the alternate qd induction motor model (AQDM) is a highly accurate mathematical model to represent the dynamic characteristics of induction motors. However, it has been shown that the variation of the rotor resistance degrades the performance of the MTPA control significantly, thus leading to its failure to satisfy the maximum torque per amp condition. To take into consideration the mismatch between the actual value of the rotor resistance and its parameter value in the design of the control strategy, an adaptive MTPA control was proposed. In this work, this adaptive MTPA control is investigated in order to achieve the desired torque with the minimum stator current at multiple operating points. The experimental study showed that (i) the desired torque was accurately achieved even though there was a deviation of the order of 5% from the commanded torque value at a torque reference of 25 Nm (tracking performance), and (ii) the minimum stator current for the desired torque (maximum torque per amp condition) was consistently satisfied at multiple operating points, as the rotor temperature increased.

• Journal of the Korean Institute of Gas
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• v.18 no.3
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• pp.53-59
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• 2014

In this study, we suggest a system to build the monitoring model for compressed natural gas (CNG) stations, operated in only non-stationary modes, and perform the real-time monitoring and the abnormality diagnosis using principal component analysis (PCA) that is suitable for processing large amounts of multi-dimensional data among multivariate statistical analysis methods. We build the model by the calculation of the new characteristic variables, called as the major components, finding the factors representing the trend of process operation, or a combination of variables among 7 pressure sensor data and 5 temperature sensor data collected from a CNG station at every second. The real-time monitoring is performed reflecting the data of process operation measured in real-time against the built model. As a result of conducting the test of monitoring in order to improve the accuracy of the system and verification, all data in the normal operation were distinguished as normal. The cause of abnormality could be refined, when abnormality was detected successfully, by tracking the variables out of the score plot.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.1
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• pp.119-125
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• 2018

The particle based computational fluid dynamics (CFD) method, which follow Lagrangian approach for fluid dynamics, fluid particle behavior by tracking all particle calculation physical quantities of each particle. According to basic concept of particle based CFD method, it is difficult to satisfy continuum theory and measure influences from neighboring particle. Article number density and weight function were used to solve aforementioned issue. Difficulties continuum mean simulate non-continuum particles such as solid including granular and sand. In this regard, the particle based CFD method modified solid particle problems by replacing viscous and surface tension forces friction and drag forces. In this paper, particle interaction model for solid particle friction model implemented to simulate solid particle problems. The broken dam problem, which is common to verify particle based CFD method, used fluid or solid particles. The angle of repose was observed in the simulation results the solid particle not fluid particle.

• Journal of the Korea Computer Graphics Society
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• v.23 no.3
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• pp.1-8
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• 2017

In this paper, we introduce a technique to retarget human motion data to the humanoid body in a constrained environment. We assume that the given motion data includes detailed interactions such as holding the object by hand or avoiding obstacles. In addition, we assume that the humanoid joint structure is different from the human joint structure, and the shape of the surrounding environment is different from that at the time of the original motion. Under such a condition, it is also difficult to preserve the context of the interaction shown in the original motion data, if the retargeting technique that considers only the change of the body shape. Our approach is to separate the problem into two smaller problems and solve them independently. One is to retarget motion data to a new skeleton, and the other is to preserve the context of interactions. We first retarget the given human motion data to the target humanoid body ignoring the interaction with the environment. Then, we precisely deform the shape of the environmental model to match with the humanoid motion so that the original interaction is reproduced. Finally, we set spatial constraints between the humanoid body and the environmental model, and restore the environmental model to the original shape. To demonstrate the usefulness of our method, we conducted an experiment by using the Boston Dynamic's Atlas robot. We expected that out method can help the humanoid motion tracking problem in the future.