• The Bulletin of The Korean Astronomical Society
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• v.44 no.2
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• pp.60.2-60.2
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• 2019

Based on our experience on exoplanet transit observation, we propose the exoplanet science cases with Small Telescope Network. One is the follow-up observation for validation of exoplanet candidates. TESS(Transiting Exoplanet Survey Satellite) is pouring out exoplanet candidates in bright stars(V<15) on all the sky. Since Small Telescope Network will consist of 0.5-1m telescopes, we will expect to produce promising outcomes from the follow-up observation of bright candidates. Next is the transit time observation. By spectroscopy of space and large telescopes during transit event, it can be possible to find the bio signatures in exoplanet atmosphere. So, in terms of cost, it is critical to determine the exact time of transit event. In addition, detecting the variation of transit time can reveal another exoplanet and exomoon in the system. In order to determine the transit time and its variation, the accumulation of transit event data is more important than the quality of photometric data. We expect that it can be a challenging project of Small Telescope Network.

• Journal of Communications and Networks
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• v.17 no.5
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• pp.453-462
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• 2015

Body area networks (BANs) have emerged as an enabling technique for e-healthcare systems, which can be used to continuously and remotely monitor patients' health. In BANs, the data of a patient's vital body functions and movements can be collected by small wearable or implantable sensors and sent using shortrange wireless communication techniques. Due to the shared wireless medium between the sensors in BANs, it may be possible to have malicious attacks on e-healthcare systems. The security and privacy issues of BANs are becoming more and more important. To provide secure and correct association of a group of sensors with a patient and satisfy the requirements of data confidentiality and integrity in BANs, we propose a novel enhanced secure sensor association and key management protocol based on elliptic curve cryptography and hash chains. The authentication procedure and group key generation are very simple and efficient. Therefore, our protocol can be easily implemented in the power and resource constrained sensor nodes in BANs. From a comparison of results, furthermore, we can conclude that the proposed protocol dramatically reduces the computation and communication cost for the authentication and key derivation compared with previous protocols. We believe that our protocol is attractive in the application of BANs.

• Journal of the Korean Institute of Gas
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• v.5 no.2 s.14
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• pp.43-51
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• 2001

ln this study, a natural gas pipeline network model was established using STONER. First a map of natural gas pipeline network was drawn on STONER and then the length and diameter of the pipe were inputted. And as the specific gravity of gas flowing in the pipeline which is the value of natural gas was inputted. Finally in order to decide the pipeline variables and gas temperature, through the verification with observed real data, the possible error was minimized. For the verification, the pipeline variables and gas temperature were assumed and the pipeline network analysis was accomplished with real demand data. The square deviation of analysed pressure from observed pressure was calculated and the minimum case was selected for the optimum pipeline variables and gas temperature. Thus a proper natural gas pipeline network model for real network was established.

• Journal of Microbiology and Biotechnology
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• v.16 no.7
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• pp.1139-1143
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• 2006

Various techniques and strategies have been developed for the identification of intracellular metabolic conditions, and among them, isotope balance-based flux analysis with gas chromatography/mass spectrometry (GC/ MS) has recently become popular. Even though isotope balance-based flux analysis allows a more accurate estimation of intracellular fluxes, its application has been restricted to relatively small metabolic systems because of the limited number of measurable metabolites. In this paper, a strategy for incorporating isotope balance-based flux data obtained for a small network into metabolic flux analysis was examined as a feasible alternative allowing more accurate quantification of intracellular flux distribution in a large metabolic system. To impose GC/MS based data into a large metabolic network and obtain optimum flux distribution profile, data reconciliation procedure was applied. As a result, metabolic flux values of 308 intracellular reactions could be estimated from 29 GC/ MS based fluxes with higher accuracy.

• Korean Journal of Health Education and Promotion
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• v.33 no.3
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• pp.1-11
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• 2016

Objectives: The challenging issue of public health program is to strengthen partnership and network between health resources. This study identified the structure and characteristics of school health program network. Methods: In this paper we collected data from schools and organizations in 4 local communities in 2014 that participated to school health program. Using social network analysis techniques we measured the number of component, diameter, density, average degree, node centralization for each network. Results: We determined that networks shared some common organizational structure such as less density, low average degree, and short diameter. Networks were dominated by the health center, and directions of collaborations between nodes were mostly one-way. Conclusions: These findings can help to depict the network of school health program. The further research is necessary to define causal relationship between network effectiveness and public health outcomes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1610-1623
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• 2013

In-network caching is a key component of information-centric networking (ICN) for reducing content download time, network traffic, and server workload. Data center network (DCN) is an ideal candidate for applying the ICN design principles. In this paper, we have evaluated the effectiveness of caching placement and replacement in DCN with butterfly-topology. We also suggest a new cache placement policy based on the number of routing nodes (i.e., hop counts) through which travels the content. With a probability inversely proportional to the hop counts, the caching placement policy makes each routing node to cache content chunks. Simulation results lead us to conclude (i) cache placement policy is more effective for cache performance than cache replacement, (ii) the suggested cache placement policy has better caching performance for butterfly-type DCNs than the traditional caching placement policies such as ALWASYS and FIX(P), and (iii) high cache hit ratio does not always imply low average hop counts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.2925-2948
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• 2019

In order to improve the utilization of irrigation water resources of greenhouse tomatoes, a water-saving irrigation decision-making model based on genetic optimization T-S fuzzy neural network is proposed in this paper. The main work are as follows: Firstly, the traditional genetic algorithm is optimized by introducing the constraint operator and update operator of the Krill herd (KH) algorithm. Secondly, the weights and thresholds of T-S fuzzy neural network are optimized by using the improved genetic algorithm. Finally, on the basis of the real data set, the genetic optimization T-S fuzzy neural network is used to simulate and predict the irrigation volume for greenhouse tomatoes. The performance of the genetic algorithm improved T-S fuzzy neural network (GA-TSFNN), the traditional T-S fuzzy neural network algorithm (TSFNN), BP neural network algorithm(BPNN) and the genetic algorithm improved BP neural network algorithm (GA-BPNN) is compared by simulation. The simulation experiment results show that compared with the TSFNN, BPNN and the GA-BPNN, the error of the GA-TSFNN between the predicted value and the actual value of the irrigation volume is smaller, and the proposed method has a better prediction effect. This paper provides new ideas for the water-saving irrigation decision in greenhouse tomatoes.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.1-9
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• 2022

Ship detection in synthetic aperture radar (SAR) images is an important application in marine monitoring for the military and civilian domains. Over the past decade, object detection has achieved significant progress with the development of convolutional neural networks (CNNs) and lot of labeled databases. However, due to difficulty in collecting and labeling SAR images, it is still a challenging task to solve SAR ship detection CNNs. To overcome the problem, some methods have employed conventional data augmentation techniques such as flipping, cropping, and affine transformation, but it is insufficient to achieve robust performance to handle a wide variety of types of ships. In this paper, we present a novel and effective approach for deep SAR ship detection, that exploits label-rich Electro-Optical (EO) images. The proposed method consists of two components: a data augmentation network and a ship detection network. First, we train the data augmentation network based on conditional generative adversarial network (cGAN), which aims to generate additional SAR images from EO images. Since it is trained using unpaired EO and SAR images, we impose the cycle-consistency loss to preserve the structural information while translating the characteristics of the images. After training the data augmentation network, we leverage the augmented dataset constituted with real and translated SAR images to train the ship detection network. The experimental results include qualitative evaluation of the translated SAR images and the comparison of detection performance of the networks, trained with non-augmented and augmented dataset, which demonstrates the effectiveness of the proposed framework.

• IEMEK Journal of Embedded Systems and Applications
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• v.16 no.3
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• pp.113-117
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• 2021

The data gathering delay and the network lifetime are important indicators to measure the service quality of wireless sensor and actuator networks (WSANs). This study proposes a dynamically cluster head (CH) selection strategy and automatic scheduling scheme of collectors for prolonging the network lifetime and shorting data gathering delay in WSAN. First the monitoring region is equally divided into several subregions and each subregion dynamically selects a sensor node as CH. These can balance the energy consumption of sensor node thereby prolonging the network lifetime. Then a task allocation method based on genetic algorithm is proposed to uniformly assign tasks to actuators. Finally the trajectory of each actuator is optimized by ant colony optimization algorithm. Simulations are conducted to evaluate the effectiveness of the proposed method and the results show that the method performs better to extend network lifetime while also reducing data delay.

• Korean Management Science Review
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• v.27 no.1
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• pp.17-31
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• 2010

Patients entering an emergency care center in a hospital usually visit medical processes in different orders depending on the urgency level and the medical treatments required. We formulate the patient flows among diverse processes in an emergency care center using the Jackson network, which is one of the queueing networks, in order to evaluate the system performances such as the expected queue length and the expected waiting time. We present a case study based on actual data collected from an emergency care center in a hospital, in order to prove the validity of applying the Jackson network model in practice. After assessing the current system performances, we provide operational strategies to reduce waiting at the bottleneck processes and evaluate the impact of those strategies on the entire system.