• Restorative Dentistry and Endodontics
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• 제42권2호
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• pp.105-110
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• 2017

Objectives: This study investigated the cleaning ability of ultrasonically activated irrigation (UAI) and a novel activation system with reciprocating motion (EC, EasyClean, Easy Equipamentos $Odontol\acute{o}gicos$) when used with a relatively new chelating agent (QMix, Dentsply). In addition, the effect of QMix solution when used for a shorter (1 minute) and a longer application time (3 minutes) was investigated. Materials and Methods: Fifty permanent human teeth were prepared with K3 rotary system and 6% sodium hypochlorite. Samples were randomly assigned to five groups (n = 10) according to the final irrigation protocol: G1, negative control (distilled water); G2, positive control (QMix 1 minute); G3, QMix 1 minute/UAI; G4, QMix 1 minute/EC; G5, QMix 3 minutes. Subsequently the teeth were prepared and three photomicrographs were obtained in each root third of root walls, by scanning electron microscopy. Two blinded and pre-calibrated examiners evaluated the images using a four-category scoring system. Data were statistically analyzed using Kruskal-Wallis and Dunn tests (p < 0.05). Results: There were differences among groups (p < 0.05). UAI showed better cleaning ability than EC (p < 0.05). There were improvements when QMix was used with auxiliary devices in comparison with conventional irrigation (p < 0.05). Conventional irrigation for 3 minutes presented significantly better results than its use for 1 minute (p < 0.05). Conclusions: QMix should be used for 1 minute when it is used with UAI, since this final irrigation protocol showed the best performance and also allowed clinical optimization of this procedure.

• Restorative Dentistry and Endodontics
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• 제45권3호
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• pp.28.1-28.13
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• 2020

Objectives: To evaluate the outcome of in vitro studies comparing the effectiveness of QMix irrigant in removing the smear layer in the root canal system compared with other irrigants. Materials and Methods: The research question was developed by using Population, Intervention, Comparison, Outcome and Study design framework. Literature search was performed using 3 electronic databases PubMed, Scopus, and EBSCOhost until October 2019. Two reviewers were independently involved in the selection of the articles and data extraction process. Risk of bias of the studies was independently appraised using revised Cochrane Risk of Bias tool (RoB 2.0) based on 5 domains. Results: Thirteen studies fulfilled the selection criteria. The overall risk of bias was moderate. QMix was found to have better smear layer removal ability than mixture of tetracycline isonomer, an acid and a detergent (MTAD), sodium hypochlorite (NaOCl), and phytic acid. The efficacy was less effective than 7% maleic acid and 10% citric acid. No conclusive results could be drawn between QMix and 17% ethylenediaminetetraacetic acid due to conflicting results. QMix was more effective when used for 3 minutes than 1 minute. Conclusions: QMix has better smear layer removal ability compared to MTAD, NaOCl, Tubulicid Plus, and Phytic acid. In order to remove the smear layer more effectively with QMix, it is recommended to use it for a longer duration.

• Restorative Dentistry and Endodontics
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• 제45권2호
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• pp.23.1-23.12
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• 2020

Objectives: This study aimed to summarize the outcome of in vitro studies comparing the antibacterial effectiveness of QMix with other irrigants against Enterococcus faecalis. Materials and Methods: The research question was developed by using population, intervention, comparison, outcome, and study design framework. The literature search was performed using 3 electronic databases: PubMed, Scopus, and EBSCOhost until October 2019. The additional hand search was performed from the reference list of the eligible studies. The risk of bias of the studies was independently appraised using the revised Cochrane Risk of Bias tool (RoB 2.0). Results: Fourteen studies were included in this systematic review. The overall risk of bias for the selected studies was moderate. QMix was found to have a higher antimicrobial activity compared to 2% sodium hypochlorite (NaOCl), 17% ethylenediaminetetraacetic acid (EDTA), 2% chlorhexidine (CHX), mixture of tetracycline isonomer, an acid and a detergent (MTAD), 0.2% Cetrimide, SilverSol/H2O2, HYBENX, and grape seed extract (GSE). QMix had higher antibacterial efficacy compared to NaOCl, only when used for a longer time (10 minutes) and with higher volume (above 3 mL). Conclusions: QMix has higher antibacterial activity than 17% EDTA, 2% CHX, MTAD, 0.2% Cetrimide, SilverSol/H2O2, HYBENX, GSE and NaOCl with lower concentration. To improve the effectiveness, QMix is to use for a longer time and at a higher volume.

• 한국컴퓨터정보학회논문지
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• 제29권1호
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• pp.11-19
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• 2024

멀티에이전트는 전장 교전 상황, 무인 운송 차량 등 다양한 실제 협동 환경에 사용될 수 있다. 전장 교전 상황에서는 도메인 정보의 제한으로 즉각적인 보상(Dense Reward) 설계의 어려움이 있어 명백한 희소 보상(Sparse Reward)으로 학습되는 상황을 고려해야 한다. 본 논문에서는 전장 교전 상황에서의 아군 에이전트 간 협업 가능성을 확인하며, 희소 보상 환경인 Multi-Robot Warehouse Environment(RWARE)를 활용하여 유사한 문제와 평가 기준을 정의하고, 강화학습 라이브러리인 Ray RLlib의 QMIX 알고리즘을 사용하여 학습 환경을 구성한다. 정의한 문제에 대해 QMIX의 Agent Network를 개선하고 Random Network Distillation(RND)을 적용한다. 이를 통해 에이전트의 부분 관측값에 대한 패턴과 시간 특징을 추출하고, 에이전트의 내적 보상(Intrinsic Reward)을 통해 희소 보상 경험 획득 개선이 가능함을 실험을 통해 확인한다.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권9호
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• pp.281-288
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• 2022

산업 응용 분야에서 AGV는 공장이나 창고와 같은 대규모 산업 시설의 무거운 자재를 운송하기 위해 자주 사용된다. 특히, 주문처리 센터에서는 자동화가 가능하여 유용성이 극대화된다. 이러한 주문처리 센터와 같은 창고에서 생산성을 높이기 위해서는 AGV들의 정교한 운반 경로 제어가 요구된다. 본 논문에서는 대중적인 협력 MARL 알고리즘인 QMIX에 적용될 수 있는 구조를 제안한다. 성능은 두 종류의 주문처리 센터 레이아웃에서 세 가지의 메트릭으로 측정하였으며, 결과는 기존 QMIX의 성능과 비교하여 제시된다. 추가적으로, AGV들의 행동 패턴에 대한 가시적인 분석을 위해 훈련된 AGV들의 운반 경로를 시각화한 히트맵을 제공한다.

• 한국군사과학기술학회지
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• 제26권4호
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• pp.361-370
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• 2023

Swarm has recently become a critical component of offensive and defensive systems. Multi-agent reinforcement learning(MARL) empowers swarm systems to handle a wide range of scenarios. However, the main challenge lies in MARL's scalability issue - as the number of agents increases, the performance of the learning decreases. In this study, transfer learning is applied to advanced MARL algorithm to resolve the scalability issue. Validation results show that the training efficiency has significantly improved, reducing computational time by 31 %.