• Polymer(Korea)
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• v.31 no.4
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• pp.329-334
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• 2007

Osmotic pellet system, which is one of the oral drug delivery systems, has been developed to improve manufacturing process, reduce product cost and other problems of osmotic tablet systems. Osmotic pellet is consisted of water swellable seed layer, drug layer, and membrane layer. Among them, the membrane layer plays an important role in a control of the drug release. In this work, we examined the effect of ratio for Eudragit RL and RS on the drug release behavior. Osmotic pellet with nifedipine as a model drug was easily obtained in a good yield by fluidized bed coater. Osmotic pellet showed round morphology with a range of size $1300{\sim}1500\;{\mu}m$. In the experiment of nifedipine release, the release amount increased with the increase of the ratio of Eudragit. This is due to the fact that Eudragit RL contains more hydrophilic quaternary ammonium group than Eudragit RS. Additionally, the release amount was retarded with increasing the membrane thickness. There are no differences in the release amount measured at the different pH 1.2, 6.5, 6.8, and 7.2. In conclusion, it was found that the drug release from osmotic pellets depended on the composition ratio and coating thickness of membrane layer.

• Journal of Aerospace System Engineering
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• v.17 no.1
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• pp.42-50
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• 2023

In this study, hexagonal ferrite powder with chemical formula SrZn_2-xCo_xFe₁₆O₂₇ was synthesized by a solid-state reaction method and its electromagnetic (EM) wave absorption characteristics were evaluated in the frequency range of 0.1-18 GHz with absorber thickness range of 0 - 10 mm. Reflection loss (RL) affecting electromagnetic wave absorption performance was calculated based on the transmission line theory using measured complex permeabilities and permittivities. RL spectra were also directly measured for some samples. They were well matched with calculated results. High-frequency complex permeability characteristics were changed gradually according to the amount of Co substitution (x). The EM wave absorption frequency band could be tuned accordingly. Hexaferrite samples with x = 1.0, 1.25, and 1.5 exhibited remarkable maximum electromagnetic wave absorption performances with minimum RL (RL_min) lowered than -50 dB. They also showed a very broad frequency band (Δf > 10 GHz) in which more than 90% of the EM wave energy absorption occurred (RL ≤ -10 dB).

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.6
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• pp.345-349
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• 2013

This paper proposes a simplified generalized residual prediction (GRP) that reduces the computational complexity of spatial scalability in scalable high efficiency video coding (SHVC). GRP is a coding tool to improve the inter prediction by adding a residual signal to the inter predictor. The residual signal was created by carrying out motion compensation (MC) of both the enhancement layer (EL) and up-sampled reference layer (RL) with the motion vector (MV) of the EL. In the MC process, interpolation of the EL and the up-sampled RL are required when the MV of the EL has sub-pel accuracy. Because the up-sampled RL has few high frequency components, interpolation of the up-sampled RL does not give significantly new information. Therefore, the proposed method reduces the computational complexity of the GRP by skipping the interpolation of the up-sampled RL. The experiment on SHVC software (SHM-2.0) showed that the proposed method reduces the decoding time by 10 % compared to conventional GRP. The BD-rate loss of the proposed method was as low as 1.0% on the top of SHM-2.0.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.97-111
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• 2015

In this paper, a methodology for effectively treating daily waste at D metropolitan city is considered using reverse logistics(RL) network. Currently, eight district offices at D metropolitan city are treating their daily wastes using each RL network. However, unfortunately, current method has a weakness such as inefficiency of RL network operation. Therefore, we propose a revised method for improving the inefficiency. In case study, we compare the performances of the current and revised methods using various real-life data. The analysis result shows that the revised method outperforms the current method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2573-2589
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• 2023

As 5G mobile systems carry multiple services and applications, numerous user, and application types with varying quality of service requirements inside a single physical network infrastructure are the primary problem in constructing 5G networks. Radio Access Network (RAN) slicing is introduced as a way to solve these challenges. This research focuses on optimizing RAN slices within a singular physical cell for vehicle-to-everything (V2X) and enhanced mobile broadband (eMBB) UEs, highlighting the importance of adept resource management and allocation for the evolving landscape of 5G services. We put forth two unique strategies: one being offline network slicing, also referred to as standard network slicing, and the other being Online reinforcement learning (RL) network slicing. Both strategies aim to maximize network efficiency by gathering network model characteristics and augmenting radio resources for eMBB and V2X UEs. When compared to traditional network slicing, RL network slicing shows greater performance in the allocation and utilization of UE resources. These steps are taken to adapt to fluctuating traffic loads using RL strategies, with the ultimate objective of bolstering the efficiency of generic 5G services.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.5
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• pp.709-715
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• 2015

As consumers interest in microgreens is increasing worldwide, the production of leafy microgreens uisng different LED lights was investigated in this study. The experiment was carried out to evaluate the effects of different LED lights on the composition and vitamin C contents of buckwheat microgreens. Physicochemical properties of buckwheat microgreens grown under different lights (red, blue, and white) and control exposed to a dark room were investigated. Moisture contents of buckwheat microgreens were 95.65% under white light (WL), 95.75% under blue light (BL), 90.77% under red light (RL), and 89.71% under dark room (DR). Crude ash contents of buckwheat microgreens grown under WL, DR, RL, and BL were 0.39%, 0.39%, 0.31%, and 0.37%, respectively. Crude protein contents of buckwheat microgreens grown under DR, RL, WL, and BL were 7.12%, 7.81%, 1.60%, and 2.40%, respectively. Crude fat contents of buckwheat microgreens grown under DR, BL, RL, and WL were 1.12%, 0.54%, 0.35%, and 0.22%, respectively. $^{\circ}Brix$ was the highest in microgreens grown under BL and RL and the lowest in buds grown under DR. Vitamin C content was the highest in buds grown under WL and the lowest in buds grown under BL. Total chlorophyll content was the highest in microgreens grown under RL and the lowest in buds grown under WL. For mineral content measurement of buckwheat microgreens, Ca, K, Mg, and P contents were high whereas B, Cu, and Zn contents were not detected. The mineral contents of buckwheat microgreens according to each color of light showed significant differences. These results demonstrated that treatment of different colored LED lights during cultivation was able to increase vitamin C content up to affecting the nutritional value of buckwheat microgreens.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.35.3-35
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• 2002

Reinforcement Learning (RL) is one of machine learning methods and an RL agent autonomously learns the action selection policy by interactions with its environment. At the beginning of RL research, it was limited to problems in environments assumed to be Markovian Decision Process (MDP). However in practical problems, the agent suffers from the incomplete perception, i.e., the agent observes the state of the environments, but these observations include incomplete information of the state. This problem is formally modeled by Partially Observable MDP (POMDP). One of the possible approaches to POMDPS is to use historical nformation to estimate states. The problem of these approaches is how t..

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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• pp.496-499
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• 2003

Support Vector Machines (SVMs) is applied to a practical problem as one of standard tools for machine learning. The application of Reinforcement Learning (RL) and SVMs in action of mobile robot is investigated. A technique to decide the action of autonomous mobile robot in practice is explained in the paper, The proposed method is to find n basis for good action of the system under unknown environment. In multi-dimensional sensor input, the most reasonable action can be automatically decided in each state by RL. Using SVMs, not only optimal decision policy but also generalized state in unknown environment is obtained.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.44 no.3
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• pp.302-307
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• 1999

Hamlet (PI549276) possessing 2RL was obtained by cross between a wheat cultivar ND7532 (Froid/Centurk) and a rye cultivar Chaupon. Chaupon was known to have resistant gene to biotype L of Hessian fly [Mayetiola destructor (Say)] larvae. The wheat-rye translocation line (Coker797*4/Hamlet) was also known to be resistant to biotype L of Hessian fly larvae. We analysed a set of 96 ESTs from the wheat-rye translocation line (2BS/2RL). ESTs were classified by various physiological processings, such as primary metabolism, secondary metabolism, transcription, translation, transport, signal transduction, defense, transposable element, and others. Three sequences encoding thioredoxin peroxidase, 26S rRNA, and rubisco small subunits were homologous to registered genes in rye. Although limited number of clones were used to develop ESTs, these clones and their sequence information may be useful for researchers studying general physiology and molecular biology on the translocation line.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1048-1063
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• 2020

In this study, we propose a reinforcement learning agent to control the data transmission rates of nodes in carrier sensing multiple access with collision avoidance (CSMA/CA)-based wireless networks. We design a reinforcement learning (RL) agent, based on Q-learning. The agent learns the environment using the timeout events of packets, which are locally available in data sending nodes. The agent selects actions to control the data transmission rates of nodes that adjust the modulation and coding scheme (MCS) levels of the data packets to utilize the available bandwidth in dynamically changing channel conditions effectively. We use the ns3-gym framework to simulate RL and investigate the effects of the parameters of Q-learning on the performance of the RL agent. The simulation results indicate that the proposed RL agent adequately adjusts the MCS levels according to the changes in the network, and achieves a high throughput comparable to those of the existing data transmission rate adaptation schemes such as Minstrel.