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IEEE 802.11be 네트워크에서 협력 엑세스 포인트를 위한 지능형 리소스 유닛 할당 기법
박주성(Juseong Park),박준희(Junhee Park),김민태(Mintae Kim),안민기(Minki Ahn),이인규(Inkyu Lee) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
본 논문에서는 IEEE 802.11 be 기술 중 하나로 상정되는 동시전송기술을 사용할 때, sum rate 을 높이기 위한 resource unit(RU) 할당 방법에 대하여 심층강화학습을 사용한 기법을 제안한다. 겹쳐진 영역에 있는 station들은 RU를 공유한다는 제약을 고려하였을 때, 지능형 할당 방법은 다른 기법 대비 우수한 성능을 보임을 모의실험을 통해 보인다.
Ryu, Mintae,Lee, Paengro,Kim, Jingul,Park, Heemin,Chung, Jinwook IOP 2016 Nanotechnology Vol.27 No.48
<P>Bilayer graphene (BLG) has an extensive list of industrial applications in graphene-based nanodevices such as energy storage devices, flexible displays, and thermoelectric devices. By doping slow Na<SUP>+</SUP> ions on Li-intercalated BLG, we find significantly improved thermal and electronic properties of BLG by using angle-resolved photoemission and high-resolution core level spectroscopy (HRCLS) with synchrotron photons. Our HRCLS data reveal that the adsorbed Na<SUP>+</SUP> ions on a BLG produced by Li-intercalation through single layer graphene (SLG) spontaneously intercalate below the BLG, and substitute Li atoms to form Na-Si bonds at the SiC interface while preserving the same phase of BLG. This is in sharp contrast with no intercalation of Na<SUP>+</SUP> ions on SLG though neutral Na atoms intercalate. The Na<SUP>+</SUP>-induced BLG is found to be stable upon heating up to <I>T</I>?=?400 °C, but returns to SLG when heated at <I>T</I> <SUB>d</SUB>?=?500 °C. The evolution of the <I>π</I>-bands upon doping the Na<SUP>+</SUP> ions followed by thermal annealing shows that the carrier concentration of the <I>π</I>-band may be artificially controlled without damaging the Dirac nature of the <I>π</I>-electrons. The doubled desorption temperature from that (<I>T</I> <SUB>d</SUB>?=?250 °C) of the Na-intercalated SLG together with the electronic stability of the Na<SUP>+</SUP>-intercalated BLG may find more practical and effective applications in advancing graphene-based thermoelectric devices and anode materials for rechargeable batteries.</P>
Band gap engineering for single-layer graphene by using slow Li<sup>+</sup> ions
Ryu, Mintae,Lee, Paengro,Kim, Jingul,Park, Heemin,Chung, Jinwook IOP 2016 Nanotechnology Vol.27 No.31
<P>In order to utilize the superb electronic properties of graphene in future electronic nano-devices, a dependable means of controlling the transport properties of its Dirac electrons has to be devised by forming a tunable band gap. We report on the ion-induced modification of the electronic properties of single-layer graphene (SLG) grown on a SiC(0001) substrate by doping low-energy (5 eV) Li<SUP>+</SUP> ions. We find the opening of a sizable and tunable band gap up to 0.85 eV, which depends on the Li<SUP>+</SUP> ion dose as well as the following thermal treatment, and is the largest band gap in the <I>π</I>-band of SLG by any means reported so far. Our Li 1s core-level data together with the valence band suggest that Li<SUP>+</SUP> ions do not intercalate below the topmost graphene layer, but cause a significant charge asymmetry between the carbon sublattices of SLG to drive the opening of the band gap. We thus provide a route to producing a tunable graphene band gap by doping Li<SUP>+</SUP> ions, which may play a pivotal role in the utilization of graphene in future graphene-based electronic nano-devices.</P>
Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발
박영찬(Youngchan Park),안상준(Sangjun An),김민태(Mintae Kim),김우주(Wooju Kim) 한국지능정보시스템학회 2020 지능정보연구 Vol.26 No.4
The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers