• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2783-2785
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• 2003

In multi-dimensional magnetic resonance imaging, data is obtained in the spatial frequency domain. Since the signal variation in the spatial frequency domain is much larger than that in the spatial domain, analog-to-digital converts with wide conversion bits are required. In this paper, the quantization noise in magnetic resonance imaging is analyzed. The signal-to-quantization noise ratio(SQNR) in the reconstructed image is derived from the level of quantization in the data acquisition. Since the quantization noise is proportional to the signal amplitude, it becomes more dominant in high field imaging. Using the derived formula the SQNR for several MRI systems are evaluated, and it is shown that the quantization noise can be a limiting factor in high field imaging, especially in three dimensional imaging in magnetic resonance imaging.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2006년도 하계종합학술대회
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• pp.447-448
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• 2006

In this paper, we propose tracking algorithm using multi resolution in wavelet transform domain. This algorithm consists of two steps. The first step is feature extraction that is select feature-points using 1-level wavelet transform in ROI (Region of Interest). The other step is feature tracking. Based on multi resolution of wavelet transform, we estimate a displacement between current frame and next frame on the basis of selected feature-points. Experimental results show that the proposed algorithm confirmed a better performance than a centroid tracking and correlation tracking.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권5호
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• pp.2253-2272
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• 2018

We propose a deep learning method for multi-focus image fusion. Unlike most existing pixel-level fusion methods, either in spatial domain or in transform domain, our method directly learns an end-to-end fully convolutional two-stream network. The framework maps a pair of different focus images to a clean version, with a chain of convolutional layers, fusion layer and deconvolutional layers. Our deep fusion model has advantages of efficiency and robustness, yet demonstrates state-of-art fusion quality. We explore different parameter settings to achieve trade-offs between performance and speed. Moreover, the experiment results on our training dataset show that our network can achieve good performance with subjective visual perception and objective assessment metrics.

• The Journal of Korean Physical Therapy
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• 제26권5호
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• pp.351-357
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• 2014

Purpose: The purpose of this study was to investigate level of functioning in patients with stroke using Modified Bathel Index (MBI), World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0), and ICF core-set for stroke. Methods: Sixty-four patients with stroke were recruited for this study from nine medical institutes. The ICF core-set for stroke, WHODAS 2.0, and MBI were used to collect subjects' functional levels. ICF core-set was employed here as a standard frame to observe multi-dimension of functioning, that is physiological bodily function, activity and participation (AP) in daily life, and current environmental factors (EF) in patients with stroke. WHODAS 2.0 and MBI were also used in order to have a specific functioning level for subjects. The linkage of each item in WHODAS 2.0 and MBI into the ICF core-set for stroke was examined. Pearson correlation coefficient was used for analysis of their relationships. Results: Functioning level of participants showed moderate resulting from MBI and WHODAS 2.0 ($73.48{\pm}22.27$ and $35.55{\pm}12.53$, respectively). Strong relationship was observed between ICF core-set and WHODAS 2.0, and with MBI. Each item of disability scales was obtained its linkage into ICF in the domain of AP. However, lack of correlation between MBI and ICF in the domain of EF was found due to absence of related factors. Conclusion: MBI was found to be linked mainly into ICF in the domain of AP and to have limited linkage into EF. Therefore, it should be suggested that the ICF concept frame should be used as a multi-dimensional approach to patients with stroke.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.385-388
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• 2002

In this paper, a new signature verification method is proposed. In the proposed method, on-line signature features are decomposed into multi-level signals by using the discrete wavelet transform, and then they are verified using the adaptive algorithm in time-frequency domain. Through computer simulations, the effectiveness of the proposed method is examined.

• 한국항행학회논문지
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• 제22권2호
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• pp.133-140
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• 2018

최근 항공전자시스템 개발에서 비용절감 및 안전인증을 지원하는 방법의 하나로 모델 기반 설계 기법을 사용한다. 모델 기반 설계를 이용하여 항공기 및 항공전자 장비 (아이템)의 성능 분석 및 안전성 분석을 지원하기 위하여 이종의 시뮬레이터를 연동하는 멀티 도메인 시뮬레이션 환경을 개발하였다. 대기 자료 컴퓨터와 통합 다기능 프로브를 항공기 수준에서 검증 할 수 있는 멀티 도메인 시뮬레이션 환경을 제시한다. 모델은 Simulink로 개발하였고, 항공기 수준에서 모델을 검증하기 위해서 비행시뮬레이터인 X-Plane 10을 사용하였다. 항공전자 시스템 모델기능을 항공기 수준에서 시험하였고, 모델과 비행시뮬레이터의 대기 자료 오차는 0.1%이내로 측정 되었다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1993년도 춘계학술대회 논문집
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• pp.262-266
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• 1993

The development of COPS(Computer aided Operation Planning System) needs data mapping paradigm which provides intelligent determonation of cutting conditions from the requirements of process planning side. We proposed the idea of multi-level mapping by the combination of heuristics of domain experts and mathematical abstraction of cutting condition and requirements. Mathematical mathods for the generalization of heuristics were constructed by multi-layer perceptron. DBMS for determination of cutting conditions was constructed by classification and combination of best fitted models. Triangular fuzzy number was used to process the uncertainties in heuristics of experts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.916-937
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• 2023

Most of the existing Distributed Denial-of-Service mitigation schemes in Software-Defined Networking are only implemented in the network domain managed by a single controller. In fact, the zombies for attackers to launch large-scale DDoS attacks are actually not in the same network domain. Therefore, abnormal traffic of DDoS attack will affect multiple paths and network domains. A single defense method is difficult to deal with large-scale DDoS attacks. The cooperative defense of multiple domains becomes an important means to effectively solve cross-domain DDoS attacks. We propose an efficient multi-domain DDoS cooperative defense mechanism by integrating blockchain and SDN architecture. It includes attack traceability, inter-domain information sharing and attack mitigation. In order to reduce the length of the marking path and shorten the traceability time, we propose an AS-level packet traceability method called ASPM. We propose an information sharing method across multiple domains based on blockchain and smart contract. It effectively solves the impact of DDoS illegal traffic on multiple domains. According to the traceability results, we designed a DDoS attack mitigation method by replacing the ACL list with the IP address black/gray list. The experimental results show that our ASPM traceability method requires less data packets, high traceability precision and low overhead. And blockchain-based inter-domain sharing scheme has low cost, high scalability and high security. Attack mitigation measures can prevent illegal data flow in a timely and efficient manner.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• 대한전자공학회논문지SP
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• 제38권4호
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• pp.335-343
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• 2001

기존의 웨이브릿 기반 프랙탈 압축 방법은 전 영역에 대하여 최적의 정의역을 탐색하므로, 부호화 과정에서 많은 탐색시간이 소요되는 단점이 있다. 그래서 본 논문에서는 웨이브릿 변환영역에서 SAS(Self Affine System) 기법과 다중 스케일 인자를 이용한 웨이브릿 변환 기반 프랙탈 영상 압축 방법을 제안한다. 웨이브릿 기반 영역에서 정의역과 치역을 구성하고, 각각의 치역 블럭에 대해 모든 정의역 블럭을 탐색하는 것이 아니라, 정의역 탐색과정이 필요 없는 SAS 기법을 도입하여 공간적으로 같은 위치에 있는 상위 레벨 블록을 정의역으로 선택한다 그래서 부호화 과정에서 곱셈 계산량을 감소시켜 고속 부호화를 가능하게 한다. 그리고 SAS 기법의 단점인 화질이 떨어지는 단점을 개선하기 위해, 각 레벨별로 서로 다른 스케일 인자를 사 용하여 화질을 개선한다. 그 결과 화질에는 영향을 미치지 않고 부호화 시간과 압축률이 개선되고, 점진적 전송이 가능한 알고리듬을 제안한다.