• 한국정보과학회논문지:소프트웨어및응용
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• 제31권2호
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• pp.235-244
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• 2004

본 논문에서는 한국어 구문 분석 및 구문 트리 표현을 위한 복합 레이블 생성 방법을 제안한다. 기존의 구문 트리 표현에서는 미리 정의된 구문 트리 레이블을 사용하여 구문 정보를 표현하였다. 본 논문에서는 이진 규칙하에서 품사태그 정보만을 이용하여 구문 레이블을 자동으로 생성하는 방법을 제시한다. 제안된 구문 레이블은 두 개의 하위 구성체의 품사정보를 적절히 구성하여 형성되며, 동시에 현 구성체의 상태 및 역할 정보를 표현할 수 있도록 고안되었다. 이와 같이 함으로써 품사태그 정보가 가지고 있는 정보를 그대로 구문 트리에 반영시킬 수 있었다. 또한, 품사 정보와 이진규칙만을 이용하여 구문 트리를 표현하기 때문에, 다양한 구문 규칙을 채택하고 있는 서로 다른 구문 분석기의 결과를 정규화 하는 데 적용할 수 있을 것이며, 일본어와 같은 다른 언어에도 쉽게 적용 가능하다. 약 31,080 문장에 대한 구문 분석의 결과, 79.30%의 정확도를 얻을 수 있었으며, 이는 제안된 구문 트리 표현 방법이 구문 분석기의 효율에도 좋은 영향을 미침을 보이는 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권8호
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• pp.2895-2912
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• 2014

Content-based image retrieval has been the most important technique for managing huge amount of images. The fundamental yet highly challenging problem in this field is how to measure the content-level similarity based on the low-level image features. The primary difficulties lie in the great variance within images, e.g. background, illumination, viewpoint and pose. Intuitively, an ideal similarity measure should be able to adapt the data distribution, discover and highlight the content-level information, and be robust to those variances. Motivated by these observations, we in this paper propose a probabilistic similarity learning approach. We first model the distribution of low-level image features and derive the free energy kernel (FEK), i.e., similarity measure, based on the distribution. Then, we propose a learning approach for the derived kernel, under the criterion that the kernel outputs high similarity for those images sharing the same class labels and output low similarity for those without the same label. The advantages of the proposed approach, in comparison with previous approaches, are threefold. (1) With the ability inherited from probabilistic models, the similarity measure can well adapt to data distribution. (2) Benefitting from the content-level hidden variables within the probabilistic models, the similarity measure is able to capture content-level cues. (3) It fully exploits class label in the supervised learning procedure. The proposed approach is extensively evaluated on two well-known databases. It achieves highly competitive performance on most experiments, which validates its advantages.

• 한국식품위생안전성학회지
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• 제18권3호
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• pp.101-106
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• 2003

냉장식품의 주요한 변패원일균으로서 저온세균인 Pseudomonas aeruhinosa를 최소 전처리한 후 신속히 검출할 수 있는 비표지 면역센서 시스템을 개발하였다. 수정결정전극상으로의 생물요소인 항체의 고정화는 이형이기능성 가교화제인 sulfosuccinimidyl 6-[3-(2-pyridyldithio)propionamido] hexanoate를 사용하여 항체를 티올화시킨 후 티올화된 항체를 화학흡착하여 행하였고, P. aeruginosa flagella에 대한 단클론항체를 사용하였을 때 다클론항체를 사용한 경우보다 센서감응이 우수한 것으로 나타났다. 항체가 고정화된 센서 chip과 flow형 quart crystal microbalance 계측 시스템을 이용하여 균 첨가 및 증균을 행한 10종의 모델시료에 대한 계측을 행하였다. 이 때, 시료자체에 의한 진동수변화가 52~89 Hz 범위인 반면 균 첨가 시에 나타난 진동수변화는 108~200 Hz이었고 증균시료에 의한 진동수변화는 162~222 Hz 범위로 나타났다. 시스템 안정화, 시료주입 및 정상상태이 센서반응 획득, 시스템 세척으로 이루어지는 한 주기의 센서계측에 소요된 시간은 모든 시료에 있어 30분 이내였다.

• 한국생물공학회:학술대회논문집
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• 한국생물공학회 2005년도 생물공학의 동향(XVII)
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• pp.742-742
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• 2005

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2009년도 춘계학술대회 논문집
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• pp.119-120
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• 2009

• Nuclear Engineering and Technology
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• 제29권6호
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• pp.500-507
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• 1997

The development of detection techniques is needed, in order for regulating authorities to determine whether or not a particular food sample has been irradiated, and label it accordingly so that a consumer's free choice can be exercised. The chemical and physical changes brought about in foods by practical doses of irradiation are very small, and therefore very sensitive methods are required. A number of promising approaches have been developed and evaluated. These include chemical, physical and biological methods ranging from the very simple to highly sophisticated techniques.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2013년도 제44회 동계 정기학술대회 초록집
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• pp.113-114
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• 2013

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging is a powerful technique for producing chemical images of small biomolecules (ex. metabolites, lipids, peptides) "as received" because of its high molecular specificity, high surface sensitivity, and submicron spatial resolution. In addition, matrix-assisted laser desorption and ionization time-of-flight (MALDI-TOF) imaging is an essential technique for producing chemical images of large biomolecules (ex. genes and proteins). For this talk, we will show that label-free mass imaging technique can be a platform technology for biomedical studies such as early detection/diagnostics, accurate histologic diagnosis, prediction of clinical outcome, stem cell therapy, biosensors, nanomedicine and drug screening [1-7].

• 센서학회지
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• 제20권4호
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• pp.226-229
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• 2011

Label-free detection of biomolecular interactions was performed using BioFET(Biologically sensitive Field-Effect Transistor) and SPR(Surface Plasmon Resonance). Qualitative information on the immobilization of an anti-IgG and antibody-antigen interaction was gained using the SPR analysis system. The BioFET was used to explore the pI value of the protein and to monitor biomolecular interactions which caused an effective charge change at the gate surface resulting in a drain current change. The results show that the BioFET can be a useful monitoring tool for biomolecular interactions and is complimentary to the SPR system.

• 센서학회지
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• 제30권6호
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• pp.395-407
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• 2021

The effect of the specifications of a silicon-on-insulator vertical slot optical waveguide on the sensitivity of homogeneous and surface sensing configurations for TE and TM polarization, respectively, was systematically analyzed using numerical software. The specifications were optimized based on the confinement factor and transmission power of the TE-guided mode distributed in the slot. The waveguide sensitivities of homogeneous and surface sensing were calculated according to the specifications of the optimized slot optical waveguide.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2168-2187
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• 2021

The smart grid replaces the traditional power structure with information inventiveness that contributes to a new physical structure. In such a field, malicious information injection can potentially lead to extreme results. Incorrect, FDI attacks will never be identified by typical residual techniques for false data identification. Most of the work on the detection of FDI attacks is based on the linearized power system model DC and does not detect attacks from the AC model. Also, the overwhelming majority of current FDIA recognition approaches focus on FDIA, whilst significant injection location data cannot be achieved. Building on the continuous developments in deep learning, we propose a Deep Learning based Locational Detection technique to continuously recognize the specific areas of FDIA. In the development area solver gap happiness is a False Data Detector (FDD) that incorporates a Convolutional Neural Network (CNN). The FDD is established enough to catch the fake information. As a multi-label classifier, the following CNN is utilized to evaluate the irregularity and cooccurrence dependency of power flow calculations due to the possible attacks. There are no earlier statistical assumptions in the architecture proposed, as they are "model-free." It is also "cost-accommodating" since it does not alter the current FDD framework and it is only several microseconds on a household computer during the identification procedure. We have shown that ANN-MLP, SVM-RBF, and CNN can conduct locational detection under different noise and attack circumstances through broad experience in IEEE 14, 30, 57, and 118 bus systems. Moreover, the multi-name classification method used successfully improves the precision of the present identification.