• 시스템엔지니어링학술지
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• 제15권2호
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• pp.72-78
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• 2019

Pathology is the motor that drives healthcare to understand diseases. The way pathologists diagnose diseases, which involves manual observation of images under a microscope has been used for the last 150 years, it's time to change. This paper is specifically based on tumor detection using deep learning techniques. Pathologist examine the specimen slides from the specific portion of body (e-g liver, breast, prostate region) and then examine it under the microscope to identify the effected cells among all the normal cells. This process is time consuming and not sufficiently accurate. So, there is a need of a system that can detect tumor automatically in less time. Solution to this problem is computational pathology: an approach to examine tissue data obtained through whole slide imaging using modern image analysis algorithms and to analyze clinically relevant information from these data. Artificial Intelligence models like machine learning and deep learning are used at the molecular levels to generate diagnostic inferences and predictions; and presents this clinically actionable knowledge to pathologist through dynamic and integrated reports. Which enables physicians, laboratory personnel, and other health care system to make the best possible medical decisions. I will discuss the techniques for the automated tumor detection system within the new discipline of computational pathology, which will be useful for the future practice of pathology and, more broadly, medical practice in general.

• Journal of Positioning, Navigation, and Timing
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• 제11권1호
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• pp.11-21
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• 2022

Fast Fourier transform (FFT)-based parallel acquisition techniques with reduced computational complexity have been widely used for the acquisition of binary phase shift keying (BPSK) global positioning system (GPS) signals. In this paper, we propose a low computational FFT-based fine acquisition technique, for binary offset carrier (BOC) modulated BPSK signals, that depending on the subcarrier-to-code chip rate ratio (SCR) selectively utilizes the computationally efficient frequency-domain realization of the BPSK-like technique and two-dimensional compressed correlator (BOC-TDCC) technique in the first stage in order to achieve a fast coarse acquisition and accomplishes a fine acquisition in the second stage. It is analyzed and demonstrated that the proposed technique requires much smaller mean fine acquisition computation (MFAC) than the conventional FFT-based BOC acquisition techniques. The proposed technique is one of the first techniques that achieves a fast FFT-based fine acquisition of BOC signals with a slight loss of detection probability. Therefore, the proposed technique is beneficial for the receivers to make a quick position fix when there are plenty of strong (i.e., line-of-sight) GNSS satellites to be searched.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2000년도 추계 학술대회논문집
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• pp.9-14
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• 2000

Computational methods can be classified roughly into two parts: one is the methods based on a potential flow theory, and the other is numerical solvers(CFD) based on Navier-Stockes equation. Methods based on a potential theory are more effective than CFD when the free surface effect is considered. Especially Rankine source method seems to become widespread for simulations of wave making problems. For computations of viscous flow problems, CFD techniques have rapidly been developed and have shown many successful results in the viscous flow calculation. Present paper introduces a computational system 'WAVIS' which includes a pre-processor, potential ant viscous flow solvers and a post-processor. To validate the system, the calculated results for modem commercial hull forms are compared with measurements. It is found that the results from the system are in good agreement with the experimental data, illustrating the accuracy of the numerical methods employed for WAVIS.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.20-22
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• 2007

The H.264/AVC standard developed by the joint Video Team (JVT) provides better coding efficiency than previous standards. The new emerging H.264/AVC employs variable block size motion estimation using multiple reference frame with 1/4-pel MV(Motion Vector) accuracy. These techniques are a important feature to accomplish higher coding efficiency. However, these techniques are increased overall computational complexity. To overcome this problem, this paper proposes advanced fast mode decision suited for variable block size by classifying inter mode based on Rate Distortion Optimization(RDO) technique. Proposed algorithm is going to use to implement H/W structure for fast mode decision. The experimental results shows that the proposed algorithm provides significant reduction computational complexity without any noticeable coding loss and additional computation. Entire computational complexity is decreased about 30%.

• 한국전산구조공학회논문집
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• 제24권5호
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• pp.543-551
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• 2011

본 논문에서는 해양작업 상태의 하중조건을 고려한 부유식 원유생산 저장 하역장치에 설치된 라이져 보강구조의 강도설계에 관련하여 다양한 근사화 기법 기반 설계최적화 및 그 성능을 비교하고자 한다. 설계최적화 문제는 하중조건별 구조강도의 제한조건 하에서 중량을 최소화하여 설계변수인 구조 부재치수가 결정되도록 정식화된다. 비교 연구를 위해 사용된 근사화 기법은 반응표면법 기반 순차적 근사최적화(RBSAO), 크리깅 기반 순차적 근사최적화(KBSAO), 그리고 개선된 이동최소자승법(MLSM) 기반 근사최적화 기법인 CF-MLSM와 Post-MLSM이다. RBSAO와 KBSAO의 적용을 위하여 상용프로세스 통합 설계최적화(PIDO) 코드를 사용하였다. 본 연구에 적용한 MLSM 기반 근사최적화 기법들은 제한조건의 가용성을 보장할 수 있도록 새롭게 개발되었다. 다양한 근사화 모델 기반 설계최적화 기법에 의한 결과는 설계 해의 개선 및 수렴속도 등의 수치적 성능을 기준으로 실제 비근사 설계최적화 결과와 비교 검토하였다.

• 한국환경보건학회지
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• 제50권2호
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• pp.113-124
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• 2024

Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2000년도 봄 학술발표회논문집
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• 대한음성학회지:말소리
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• 제65호
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• pp.153-168
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• 2008

We propose a new method to apply shape analysis techniques to a computational auditory scene analysis (CASA)-based speech separation system. The conventional CASA-based speech separation system extracts speech signals from a mixture of speech and noise signals. In the proposed method, we complement the missing speech signals by applying the shape analysis techniques such as labelling and distance function. In the speech separation experiment, the proposed method improves signal-to-noise ratio by 6.6 dB. When the proposed method is used as a front-end of speech recognizers, it improves recognition accuracy by 22% for the speech-shaped stationary noise condition and 7.2% for the two-talker noise condition at the target-to-masker ratio than or equal to -3 dB.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2004년도 가을 학술발표회 논문집
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• pp.245-253
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• 2004

There has been recent demand for extending the life of age-degraded structures and equipment by such techniques as diagnosis, maintenance, safety assessment, and estimating residual life on iron-making plants and hydraulic, thermal, and nuclear power plants. These techniques take into account comprehensive scenarios that may cause malfunction and structural damage and allow an assessment of risk based on the likely scenarios. In particular the safety assessment and residual life estimation of age-degraded ships and equipment facilities require consideration of various factors such as mechanical and thermal stresses, corrosion, hardness, load variation due to changes of operating condition, crack generation and strength reduction of material by fatigue. In this study, a detail thermal stress analysis, one of useful techniques of safety assessment and maintenance, is performed on a blast furnace by using general FEM code (MSC/NASTRAN).

• Spatial Information Research
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• 제2권2호
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• pp.175-188
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• 1994

본 연구에서는 최근 많은 분야데서 패턴인식을 위한 효과적인 기법으로 이용되고 있는 신경망 기법을 원격탐사자료의 군집화 기법으로서 적용하고자 하였다. 이를 위해 선택된 신경망 모델은 경쟁학습 신경망이며 이를 구성하는 각종 변수들을 재구성하여 원격탐사자료의 군집화를 위한 신경망모델을 설정하였다. 본 신경망을 이용한 군집화 기법은 항공기를 이용하여 획득된 원격탐사자료를 이용하여 순차적(sequential)군집화 기법 K 평균 군집화 기법과 비교되었다. 계산시간은 순차적 기법이나 K 평균기법에 비하여 더 많이 소요되나 정확도면에 있어서는 비교적 우수한 결과를 나타냈다.