• Journal of Ginseng Research
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• v.45 no.2
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• pp.305-315
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• 2021

Background: Panax stipuleanatus represents a folk medicine for treatment of inflammation. However, lack of experimental data does not confirm its function. This article aims to investigate the analgesic and anti-inflammatory activities of triterpenoid saponins isolated from P. stipuleanatus. Methods: The chemical characterization of P. stipuleanatus allowed the identification and quantitation of two major compounds. Analgesic effects of triterpenoid saponins were evaluated in two models of thermal- and chemical-stimulated acute pain. Anti-inflammatory effects of triterpenoid saponins were also evaluated using four models of acetic acid-induced vascular permeability, xylene-induced ear edema, carrageenan-induced paw edema, and cotton pellet-induced granuloma in mice. Results: Two triterpenoid saponins of stipuleanosides R₁ (SP-R₁) and R₂ (SP-R₂) were isolated and identified from P. stipuleanatus. The results showed that SP-R₁ and SP-R₂ significantly increased the latency time to thermal pain in the hot plate test and reduced the writhing response in the acetic acid-induced writhing test. SP-R₁ and SP-R₂ caused a significant decrease in vascular permeability, ear edema, paw edema, and granuloma formation in inflammatory models. Further studies showed that the levels of inflammatory mediators, nitric oxide, malondialdehyde, tumor necrosis factor-α, and interleukin 6 in paw tissues were downregulated by SP-R₁ and SP-R₂. In addition, the rational harvest of three- to five-year-old P. stipuleanatus was preferable to obtain a higher level of triterpenoid saponins. SP-R₂ showed the highest content in P. stipuleanatus, which had potential as a chemical marker for quality control of P. stipuleanatus. Conclusion: This study provides important basic information about utilization of P. stipuleanatus resources for production of active triterpenoid saponins.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• The Korea Journal of Herbology
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• v.36 no.2
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• pp.1-9
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• 2021

Objectives : Gossypium arboreum (cotton) is traditionally used to treat various health disorders. However, anti-amnesic effect of G. arboreum has not been reported. The objective of this study was to investigate in-vivo the anti-amnesic effects along with in vitro antioxidant and acetylcholinesterase (AChE) inhibition potential in G. arboreum seed essential oil. Methods : The essential oil of G. arboreum obtained by solid phase microextraction (SPME) techniques were identified by gas chromatography-mass spectroscopy (GC-MS). 2,2-diphenyl-1-picrylhydrazyl (DPPH) and 2,2'-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) assay were performed to determine the antioxidant activity at various concentrations (312.5, 625, 1250, 2500, 5000, 10000 ㎍/㎖. Y-maze, passive avoidance and Morris water maze tests were carried out to evaluate improved effect on scopolamine (1 mg/kg)-induced memory dysfunction at the dose level of 50, 100 and 200 mg/kg. Donepezil (5 mg/kg) was used as a positive drug control. We performed acetylcholinesterase (AChE) activity assay in ex vivo. Results : Five volatile compounds were identified in G. arboreum. The assays of DPPH and ABTS revealed that G. arboreum increased antioxidant activity in a dose-dependent manner. G. arboreum ameliorated the percent of spontaneous alternation in the Y-maze test, shortened step-through latency in the passive avoidance test, and increased swimming time in the target zone in the Morris water maze test. In addition, G. arboreum inhibited the AChE activity. Conclusions : Based on these findings, G. arboreum may aid in the prevention and treatment of learning and memory-deficit disorders through antioxidant and AChE inhibitory activities.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.140-142
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• 2021

Mobile communication technology after 5th generation requires high speed, hyper-connection, and low latency communication. In order to meet technical requirements for secure hyper-connectivity, low-spec IoT devices that are considered the end of IoT services must also be able to provide the same level of security as high-spec servers. For the purpose of performing these security functions, it is required for cryptographic keys to have the necessary degree of stability in cryptographic algorithms. Cryptographic keys are usually generated from cryptographic random number generators. At this time, good noise sources are needed to generate random numbers, and hardware random number generators such as TRNG are used because it is difficult for the low-spec device environment to obtain sufficient noise sources. In this paper we used the chip which is based on quantum characteristics where the decay of radioactive isotopes is unpredictable, and we presented a variety of methods (TRNG) obtaining an entropy source in the form of binary-bit series. In addition, we conducted the NIST SP 800-90B test for the entropy of output values generated by each TRNG to compare the amount of entropy with each method.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.393-401
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• 2024

Recently, the Internet of Things (IoT) has been widely used in industries and daily life that directly affect human safety, life, and assets. However, IoT devices, which need to meet low-cost, lightweight, and low-power requirements, face a significant problem of shortened battery lifetime due to battery draining attacks and interference. To solve this problem, the 802.11ba standard for the Wake-up Receiver (WuR) has emerged, this feature is playing a crucial role in minimizing energy consumption. However, the WuR protocol did not consider security mechanisms in order to reduce latency and overhead. Therefore, in this study, anAdaptive Power Saving Mechanism (APSM) is proposed for low-power WuR to counter battery draining attacks. APSM can minimize abnormally occurring power consumption by exponentially increasing power-saving time in environments prone to attacks. According to experimental results, the proposed APSM improved energy consumption efficiency by a minimum of 13.77% compared to the traditional Legacy Power Saving Mechanism (LPSM) when attack traffic ratio is 10% or more of the total traffic.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.42 no.4 s.334
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• pp.19-28
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• 2005

In this paper, we proposed a parallel fast 2-D discrete wavelet transform hardware architecture based on lifting scheme. The proposed architecture improved the 2-D processing speed, and reduced internal memory buffer size. The previous lifting scheme based parallel 2-D wavelet transform architectures were consisted with row direction and column direction modules, which were pair of prediction and update filter module. In 2-D wavelet transform, column direction processing used the row direction results, which were not generated in column direction order but in row direction order, so most hardware architecture need internal buffer memory. The proposed architecture focused on the reducing of the internal memory buffer size and the total calculation time. Reducing the total calculation time, we proposed a 4-way data flow scheduling and memory based parallel hardware architecture. The 4-way data flow scheduling can increase the row direction parallel performance, and reduced the initial latency of starting of the row direction calculation. In this hardware architecture, the internal buffer memory didn't used to store the results of the row direction calculation, while it contained intermediate values of column direction calculation. This method is very effective in column direction processing, because the input data of column direction were not generated in column direction order The proposed architecture was implemented with VHDL and Altera Stratix device. The implementation results showed overall calculation time reduced from $N^2/2+\alpha$ to $N^2/4+\beta$, and internal buffer memory size reduced by around $50\%$ of previous works.

• Progress in Medical Physics
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• v.20 no.3
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• pp.132-138
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• 2009

Studies on target motion in 4-dimensional radiotherapy are being world-widely conducted to enhance treatment record and protection of normal organs. Prediction of tumor motion might be very useful and/or essential for especially free-breathing system during radiation delivery such as respiratory gating system and tumor tracking system. Neural network is powerful to express a time series with nonlinearity because its prediction algorithm is not governed by statistic formula but finds a rule of data expression. This study intended to assess applicability of neural network method to predict tumor motion in 4-dimensional radiotherapy. Scaled Conjugate Gradient algorithm was employed as a learning algorithm. Considering reparation data for 10 patients, prediction by the neural network algorithms was compared with the measurement by the real-time position management (RPM) system. The results showed that the neural network algorithm has the excellent accuracy of maximum absolute error smaller than 3 mm, except for the cases in which the maximum amplitude of respiration is over the range of respiration used in the learning process of neural network. It indicates the insufficient learning of the neural network for extrapolation. The problem could be solved by acquiring a full range of respiration before learning procedure. Further works are programmed to verify a feasibility of practical application for 4-dimensional treatment system, including prediction performance according to various system latency and irregular patterns of respiration.

• Sleep Medicine and Psychophysiology
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• v.14 no.1
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• pp.20-25
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• 2007

The present study compared the actigraphic indices between both wrist actigraphies (WATGs), and the sleep estimates between each WATG and nocturnal polysomnography (NPSG) to assess their differences and consistencies. We studied 22 right-handed subjects (mean age $43.9{\pm}13.3\;years$, M：F=14：8) with untreated primary sleep disorders (primary insomnia=8, simple snorer=2, obstructive sleep apnea=12) undergone by overnight both WATGs and NPSG, simultaneously. Comparison and correlation were analyzed between right and left wrist actigraphic data. In the sleep estimates of both WATGs and NPSG, each WATG was compared and correlated with NPSG in sleep period time (SPT), total sleep time (TST), sleep latency (SL), sleep efficiency (SE) and wake time (WT). Sleep indices between both WATGs showed significant positive correlations with no correlations in SL and fragmentation index (FI). There were no differences in sleep indices between both WATGs. SPTs of both WATGs, SL of left WATG, and TST of right WATG showed positively significant correlations, and SE of right WATG did negatively significant correlation in sleep indices between each WATG and NPSG. As each WATG was compared to PSG, SPTs of both WATGs and WT of right WATG were decreased, and TST and SE of right WATG and SL of left WATG were increased. Inconsistent SL and FI between both WATGs indicate that the activities between both WATGs can differentially happen during wake or arousal. Inconsistent sleep estimates between each WATG and NPSG may indicate the limited usefulness in measuring and analyzing one-night sleep by using WATG.

• Sleep Medicine and Psychophysiology
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• v.28 no.2
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• pp.70-77
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• 2021

Objectives: Our study aims to investigate the clinical and polysomnographic variables associated with subjective sleep perception. Methods: Among the patients who underwent nocturnal polysomnography (PSG) at the Center for Sleep and Chronobiology of Seoul National University Hospital from May 2018 to July 2019, 109 diagnosed with insomnia disorder based on DSM-5 were recruited for the study, and their medical records were retrospectively analyzed. Self-report questionnaires about clinical characteristics including Pittsburgh sleep quality index (PSQI), Beck depression inventory (BDI), and Epworth sleepiness scale (ESS) were completed. Subjective sleep quality was measured using variables of subjective total sleep time (subjective TST), subjective sleep onset latency (subjective SOL), subjective number of awakenings, morning feeling after awakening, and sleep discrepancy (subjective TST-objective TST) the morning after PSG. Pearson and Spearman correlation analyses were used to determine the factors associated with subjective sleep perception. Results: In patients with insomnia, subjective TST was negatively correlated with Wake After Sleep Onset (WASO) (p = 0.001) and N1 sleep (p = 0.039) parameters on polysomnography. Also, it was negatively correlated with PSQI (p < 0.001) and BDI (p = 0.014) scores. Sleep discrepancy was negatively correlated with PSQI score (p = 0.018). Morning feeling was negatively correlated with PSQI (p = 0.019) and BDI (p < 0.001) scores. Conclusion: Our results demonstrated that subjective sleep perception is associated with PSG variables (WASO and N1 sleep) and with PSQI and BDI scores. In clinical practice, it is helpful to assess and manage insomnia patients in consideration of objective sleep variables, subjective sleep quality, and depressed mood, which can influence subjective sleep perception.

• Sleep Medicine and Psychophysiology
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• v.24 no.2
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• pp.106-117
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• 2017

Objectives: Obstructive sleep apnea (OSA) is a sleep-related breathing disorder that is characterized by repetitive collapse or partial collapse of the upper airway during sleep in spite of ongoing effort to breathe. It is believed that OSA is usually worsened in REM sleep, because muscle tone is suppressed during REM sleep. However, many cases showed a higher apnea-hypopnea index (AHI) during NREM sleep than during REM sleep. We aimed here to determine the characteristics of REM sleep-dependent OSA (REM-OSA) and NREM sleep-dependent OSA (NREM-OSA). Methods: Five hundred sixty polysomnographically confirmed adult OSA subjects were studied retrospectively. All patients were classified into 3 groups based on the ratio between REM-AHI and NREM-AHI. REM-OSA was defined as REM-AHI/NREM-AHI > 2, NREM-OSA as NREM-AHI/REM-AHI > 2, and the rest as sleep stage-independent OSA (IND-OSA). In addition to polysomnography, questionnaires related to subjective sleep quality, daytime sleepiness, and emotion were completed. Chi-square test, ANOVA, and ANCOVA were performed. Results: There was no age difference among subgroups. The REM-OSA group was comprised of large proportions of mild OSA and female OSA patients. These patients experienced poor sleep and more negative emotions than other two groups. The AHI and oxygen desaturation index (ODI) were lowest in REM-OSA. Sleep efficiency and N3 percentage of REM-OSA were higher than in NREM-OSA. The percentage of patients who slept in a supine position was higher in REM-OSA than other subgroups. IND-OSA showed higher BMI and larger neck circumference and abdominal circumference than REM-OSA. The patients with IND-OSA experienced more sleepiness than the other groups. AHI and ODI were highest in IND-OSA. NREM-OSA presented the shortest total sleep time and the lowest sleep efficiency. NREM-OSA showed shorter sleep latency and REM latency and higher percentage of N1 than those of REM-OSA and the highest proportion of those who slept in a lateral position than other subgroups. NREM-OSA revealed the highest composite score on the Horne and ${\ddot{O}}stberg$ questionnaire. With increased AHI severity, the numbers of apnea and hypopnea events during REM sleep decreased, and the numbers of apnea and hypopnea events during NREM sleep increased. The results of ANCOVA after controlling age, sex, BMI, NC, AC, and AHI showed the lowest sleep efficiency, the highest AHI in the supine position, and the highest percentage of waking after sleep onset in NREM-OSA. Conclusion: REM-OSA was associated with the mild form of OSA, female sex, and negative emotions. IND-OSA was associated with the severe form of OSA. NREM-OSA was most closely related to position and showed the lowest sleep efficiency. Sleep stage-dependent characteristics could provide better understanding of OSA.