• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.33 no.4B
• /
• pp.165-173
• /
• 2008

In this paper, we propose a new reliable broadcast protocol referred to as timer-based reliable broadcast (TRB) for wireless sensor networks (WSNs). The proposed TRB scheme exploits (1) bitmap based explicit ACK to effectively reduce the unnecessary error control messages and (2) randomized timer for ACK transmission to substantially reduce the possibility of contentions. Although it has been argued that 100% reliability is not necessary in WSNs, there should be messages (such as mission-critical message, task assignment, software updates, etc.) that need to be reliably delivered to the entire sensor field. We propose to use the TRB algorithm for such cases. Performance evaluation shows that the TRB scheme achieves 100 % reliability significantly better than other schemes with expense of slightly increased energy consumption.

• The Journal of Korean Obstetrics and Gynecology
• /
• v.21 no.4
• /
• pp.193-206
• /
• 2008

Purpose: In spite of many arguments on the result of WHI (Women's Health Initiative) study, no one can deny the necessity for researches on the alternative treatment to HRT (hormone replacement therapy). In this study, the author wanted to investigate the method of precedent acupuncture RCTs (randomized controlled trials) to make out the appropriate acupuncture study design on postmenopausal and perimenopausal hot flashes in Korea. Methods: Precedent studies were investigated using Pubmed search and key-words "acupuncture and hot flash", "acupuncture and menopause", "acupuncture and vasomotor", limited to RCT, from 2000 to 2008 April. Results: As a result. 10 studies were searched. In the hereafter studies, multi-center clinical trials which consist of population group of postmenopausal and perimenopausal women that would be pre-stratified and more than 50 patients per treatment arm seem adequate. Sham control study can make out the proper consequence because many people are get used to acupuncture in Korea. Flexible choice of acupoints addressed an individual's symptoms using standardized algorithm is recommended. Treatment consist of 4 weeks' observation, 11 acupuncture sessions during 7 weeks, follow-up of 3 months or more after treatment and hot flash score as a primary outcome measure seem appropriate. After all, higher level of description according to global standard must be obtained in the study report and publishing. Conclusion: The researchers should develope the methodology of acupuncture clinical trial on the postmenopausal and perimenopausal hot flashes.

• Current Optics and Photonics
• /
• v.3 no.2
• /
• pp.119-127
• /
• 2019

A new secret-key-sharing cryptosystem using optical phase-shifting digital holography is proposed. The proposed secret-key-sharing algorithm is based on the Diffie-Hellman key-exchange protocol, which is modified to an optical cipher system implemented by a two-step quadrature phase-shifting digital holographic encryption method using orthogonal polarization. Two unknown users' private keys are encrypted by two-step phase-shifting digital holography and are changed into three digital-hologram ciphers, which are stored by computer and are opened to a public communication network for secret-key-sharing. Two-step phase-shifting digital holograms are acquired by applying a phase step of 0 or ${\pi}/2$ in the reference beam's path. The encrypted digital hologram in the optical setup is a Fourier-transform hologram, and is recorded on CCDs with 256 quantized gray-level intensities. The digital hologram shows an analog-type noise-like randomized cipher with a two-dimensional array, which has a stronger security level than conventional electronic cryptography, due to the complexity of optical encryption, and protects against the possibility of a replay attack. Decryption with three encrypted digital holograms generates the same shared secret key for each user. Schematically, the proposed optical configuration has the advantage of producing a kind of double-key encryption, which can enhance security strength compared to the conventional Diffie-Hellman key-exchange protocol. Another advantage of the proposed secret-key-sharing cryptosystem is that it is free to change each user's private key in generating the public keys at any time. The proposed method is very effective cryptography when applied to a secret-key-exchange cryptosystem with high security strength.

• The Bulletin of The Korean Astronomical Society
• /
• v.44 no.2
• /
• pp.55.3-55.3
• /
• 2019

We present a pipeline to estimate baryonic properties of a galaxy inside a dark matter (DM) halo in DM-only simulations using a machine trained on high-resolution hydrodynamic simulations. As an example, we use the IllustrisTNG hydrodynamic simulation of a (75 h^-1 Mpc)³ volume to train our machine to predict e.g., stellar mass and star formation rate in a galaxy-sized halo based purely on its DM content. An extremely randomized tree (ERT) algorithm is used together with multiple novel improvements we introduce here such as a refined error function in machine training and two-stage learning. Aided by these improvements, our model demonstrates a significantly increased accuracy in predicting baryonic properties compared to prior attempts --- in other words, the machine better mimics IllustrisTNG's galaxy-halo correlation. By applying our machine to the MultiDark-Planck DM-only simulation of a large (1 h^-1 Gpc)³ volume, we then validate the pipeline that rapidly generates a galaxy catalogue from a DM halo catalogue using the correlations the machine found in IllustrisTNG. We also compare our galaxy catalogue with the ones produced by popular semi-analytic models (SAMs). Our so-called machine-assisted semi-simulation model (MSSM) is shown to be largely compatible with SAMs, and may become a promising method to transplant the baryon physics of galaxy-scale hydrodynamic calculations onto a larger-volume DM-only run. We discuss the benefits that machine-based approaches like this entail, as well as suggestions to raise the scientific potential of such approaches.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.45 no.10
• /
• pp.40-48
• /
• 2008

Security of self organized mobile ad hoc networks is an important issue because administration information in the networks is managed by the constituent nodes. Especially authentication mechanism is necessary for trust setup between newly joining nodes and the network. The authentication models and protocols which are based on the wireline infrastructure could not be practical for mobile ad hoc network. Although public key algorithm-based method is widely used for authentication, it is not easy to be applied to mobile ad hoc networks because they do not have infrastructure such as centralized CA which is needed for certificate verification. In this paper, we consider the public key based random CA group method proposed in [1] to provide efficient authentication scheme to mobile ad hoc networks and analyze the performance of the method, which is then compared to the home CA method. From the analysis results, we see that the random CA method where the function of CA is distributed to some mobile nodes and the authentication information is propagated to randomly chosen CAs shows higher reliability and lower cost than home CA method.

• The Plant Pathology Journal
• /
• v.34 no.5
• /
• pp.381-392
• /
• 2018

Clavibacter michiganensis subsp. michiganesis (Cmm) is a quarantine-worthy pest in $M{\acute{e}}xico$. The implementation and validation of new technologies is necessary to reduce the time for bacterial detection in laboratory conditions and Raman spectroscopy is an ambitious technology that has all of the features needed to characterize and identify bacteria. Under controlled conditions a contagion process was induced with Cmm, the disease epidemiology was monitored. Micro-Raman spectroscopy ($532nm\;{\lambda}$ laser) technique was evaluated its performance at assisting on Cmm detection through its characteristic Raman spectrum fingerprint. Our experiment was conducted with tomato plants in a completely randomized block experimental design (13 plants ${\times}$ 4 rows). The Cmm infection was confirmed by 16S rDNA and plants showed symptoms from 48 to 72 h after inoculation, the evolution of the incidence and severity on plant population varied over time and it kept an aggregated spatial pattern. The contagion process reached 79% just 24 days after the epidemic was induced. Micro-Raman spectroscopy proved its speed, efficiency and usefulness as a non-destructive method for the preliminary detection of Cmm. Carotenoid specific bands with wavelengths at 1146 and $1510cm^{-1}$ were the distinguishable markers. Chemometric analyses showed the best performance by the implementation of PCA-LDA supervised classification algorithms applied over Raman spectrum data with 100% of performance in metrics of classifiers (sensitivity, specificity, accuracy, negative and positive predictive value) that allowed us to differentiate Cmm from other endophytic bacteria (Bacillus and Pantoea). The unsupervised KMeans algorithm showed good performance (100, 96, 98, 91 y 100%, respectively).

• The Korean Journal of Applied Statistics
• /
• v.34 no.3
• /
• pp.309-327
• /
• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.