• 디지털산업정보학회논문지
• /
• 제4권2호
• /
• pp.21-28
• /
• 2008

There are some problems on the system that uses a password comprising a digital signature to identify the secret key owner under the public key infrastructure. For example, the password can be difficult to remember or easy to be disclosure, and users should make more complex password to protect it. A number of studies have been proceeded in order to overcome these defects using the fingerprint identification technologies, but they need to change the current standard of public key infrastructure. On the suggested private key escrow system, the private key can be withdrawn only through the enrollment and identification of a fingerprint template after it is saved to a reliable third system. Therefore, this new private key escrow system can remove previous inconveniences of managingthe private key on current public key infrastructure, and it exhibited superior results in terms of the evaluation items when compared with the integrated method of the existing fingerprint identification and public key infrastructure.

• Journal of information and communication convergence engineering
• /
• 제10권3호
• /
• pp.242-247
• /
• 2012

In the determination of suitable frame sizes associated with dynamic framed slotted Aloha used in radio frequency identification tag identification processes, the widely imposed constraint $L=2^Q$ often yields inappropriate values deviating far from the optimal values, while a straightforward use of the estimated optimal frame sizes causes frequent restarts of read procedures, both resulting in long identification delays. Taking a trade-off, in this paper, we propose a new method for determining effective frame sizes where the effective frame size changes in a multiple of a predetermined step size, namely ${\Delta}$. Through computer simulations, we show that the proposed scheme works fairly well in terms of identification delay.

• 한국지능정보시스템학회:학술대회논문집
• /
• 한국지능정보시스템학회 2005년도 공동추계학술대회
• /
• pp.183-192
• /
• 2005

Recently, community computing has been proposed for group formation and group decision-making. However, legacy community computing systems do not support group need identification for ad hoc group formation, which would be one of key features of ubiquitous decision support systems and services. Hence, this paper aims to provide a multi-agent based methodology to enable nomadic community computing which supports ad hoc need identification and group formation. Focusing on supporting group decision-making of relatively small sized multiple individual in a community, the methodology copes with the following three characteristics: (1) ad hoc group formation, (2) context-aware group need identification, and (3) using mobile devices working in- and out-doors. NAMA-US, an RFID-based prototype system, has been developed to show the feasibility of the idea proposed in this paper.

• 정보보호학회논문지
• /
• 제19권6호
• /
• pp.195-199
• /
• 2009

이 논문에서는 CDH가정에 안전성을 기초로 하는 공격자가 참여하는 새로운 문제를 정의하고 이의 안전성을 증명한다. 이 새로운 문제는 암호 프로토콜의 설계에서 프리미티브로 이용될 수 있다. 이 논문에서는 이 문제의 응용 예로 새로운 identification 기법을 보인다. 또한, 이 문제의 판별 버전(decisional version)에 대해서도 살펴본다.

• Journal of Preventive Medicine and Public Health
• /
• 제54권3호
• /
• pp.218-219
• /
• 2021

The identification of human remains can be performed visually through families and next-of-kin, but it is not advisable to rely only on visual recognition; instead, it is preferable to conduct a forensic comparison of antemortem and postmortem data for primary identifiers (fingerprints, DNA, and dental data). A dental autopsy is particularly valuable in the identification process of skeletonized, carbonized, saponified, and fragmented human remains. The principal challenge in the identification process is the search and collection of antemortem data. To this end, all dental information held on a missing person can represent a precious source of individualizing information that families should share with the police or investigating agencies after reporting a disappearance.

• 한국컴퓨터정보학회논문지
• /
• 제22권2호
• /
• pp.1-7
• /
• 2017

Recently, NAND flash memory, which is used as a storage medium, is replacing HDD (Hard Disk Drive) at a high speed due to various advantages such as fast access speed, low power, and easy portability. In order to apply NAND flash memory to a computer system, a Flash Translation Layer (FTL) is indispensably required. FTL provides a number of features such as address mapping, garbage collection, wear leveling, and hot data identification. In particular, hot data identification is an algorithm that identifies specific pages where data updates frequently occur. Hot data identification helps to improve overall performance by identifying and managing hot data separately. MHF (Multi hash framework) technique, known as hot data identification technique, records the number of write operations in memory. The recorded value is evaluated and judged as hot data. However, the method of counting the number of times in a write request is not enough to judge a page as a hot data page. In this paper, we propose hot data identification which considers not only the number of write requests but also the persistence of write requests.

• Journal of Information Processing Systems
• /
• 제16권2호
• /
• pp.435-446
• /
• 2020

With the ability to learn rules from training data, the machine learning model can classify unknown objects. At the same time, the dimension of hyperspectral data is usually large, which may cause an over-fitting problem. In this research, an identification methodology of tea diseases was proposed based on spectral reflectance and machine learning, including the feature selector based on the decision tree and the tea disease recognizer based on random forest. The proposed identification methodology was evaluated through experiments. The experimental results showed that the recall rate and the F1 score were significantly improved by the proposed methodology in the identification accuracy of tea disease, with average values of 15%, 7%, and 11%, respectively. Therefore, the proposed identification methodology could make relatively better feature selection and learn from high dimensional data so as to achieve the non-destructive and efficient identification of different tea diseases. This research provides a new idea for the feature selection of high dimensional data and the non-destructive identification of crop diseases.

• 한국정보통신학회논문지
• /
• 제16권5호
• /
• pp.973-981
• /
• 2012

본 논문에서는 비선형 시스템에 대하여 Takagi-sugeno(T-S) 퍼지 Identification을 이용하여 유도전동기의 비선형 다변수 시스템을 선형화 할 수 있는 새로운 방법을 제안한다. T-S 퍼지 모델의 선형화는 퍼지 규칙들 및 소속 함수들의 산술적인 계산으로 인해 선형화가 쉽지 않다. 그러므로 T-S 퍼지 Identification을 이용하여 퍼지 규칙 및 소속함수들의 추정을 통해 높은 정확도를 가지는 선형 모델로 제공한다.

• 정보보호학회논문지
• /
• 제14권6호
• /
• pp.79-89
• /
• 2004

지문 식별은 데이터베이스에 사용자의 지문 정보를 저장하는 사용자 등록 과정과 입력된 사용자의 지문 정보에 대하여 유사한 후보자 목록을 작성하는 식별 과정으로 구성된다. 그러나 대규모 데이터베이스에 저장된 사용자 지문 정보를 전체적으로 순차 검색할 경우 오랜 수행 시간이 요구된다는 문제가 있다. 이러한 문제점을 해결하기 위하여 본 논문에서는 실시간으로 지문을 식별하기 위한 지문 식별 전용 하드웨어를 설계하였다. 설계된 지문 식별 전용 하드웨어는 PCI 보드에 대용량 FPGA와 SDRAM을 실장하여 지문 검색을 병렬로 처리한다. 설계된 하드웨어에 대한 성능평가 결과, 등록자 수 증가에 따른 높은 확장성을 보이며 지문 식별을 실시간에 처리할 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제16권5호
• /
• pp.1755-1777
• /
• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.