• International Journal of Computer Science & Network Security
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• 제23권7호
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• 한국컴퓨터정보학회논문지
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• 제4권3호
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• pp.13-19
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• 1999

본 연구는 TPN (Time Petri Nets) unfolding을 이용하여 가공 machine과 세정 machine으로 구성된 FMC (Flexible Manufacturing Cell)의 WIP (Work In Process)를 분석하는 방법을 제시한다. 여기서, PN의 unfolding은 상태공간폭발이 발생하지 않는 concurrent system의 검증에 사용되는 순서기반 방법이다. 본 연구는 일반적으로 발생하는 순환상태 스케쥴 문제에서 가장 그 작업과정 시간을 최적화하기 위하여 원래의 net을 동일한 비순환 net으로 바꾸어 줄 수 있는 unfolding 개념을 기반으로 한 것이다.

• Journal of the Korean Wood Science and Technology
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• 제13권4호
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• pp.67-72
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• 1985

To improve the bending strength of sawdustboard, verious resin contents of 10, 13, 16, and 19% were applied to the thin shell (face layer) composed with wire net or not. The shell effect of sawdust and wire net composition formed with core sawdustboard were evaluated. Forcusing on the effects of wire net composition and noncomposition including a comparison with chipboard and veneer complyboard, bending properties (Modulus of rupture (MOR), Modulus of elasticity (MOE), Stress at proportional limit ($S_{pl}$). Work to maximum load ($W_{ml}$))were analyzed and discussed. 1. In modulus of rutpute, veneer comply was the highest (621.5 kg/$cm^2$), and next decreasing order was wire net composition (159.1 kg/$cm^2$), chipboard (81.75 kg/$cm^2$), and wire net noncomposition (76.21 kg/$cm^2$) as in modulus of elasticity, work to maximum load, except for stress at proportional limit. 2. The highly significant effects were shown in both wire net composition and noncomposition, at the same time wire net composition exceeded two times of noncomposition throughout resin contents in bending properties. Chipboard was similar to the mean or 16% resin content in noncomposirion. 3. Every board in wire net composition above 10% resin content was beyond 100 kg/$cm^2$ in MOR, minimum allowable strength for structural use according to KS F 3104. In conclusion, the feasibility for improving the bending strength of weak sawdustboard by wire net composed shell was offered.

• 한국시뮬레이션학회논문지
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• 제30권4호
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• pp.1-8
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• 2021

복잡하고 비선형적인 특징을 갖는 시계열 데이터를 예측하기 위해 딥러닝 기법이 널리 사용되고 있다. 본 연구에서는 최근에 개발된 WaveNet을 개선하고 워크포워드 검증 기법을 적용하여 전력 소비량 데이터를 24시간 이전에 예측하고자 한다. 원래 WaveNet은 오디오 데이터 예측에 사용하고자 고안되었으며, 장기간의 데이터를 효과적으로 예측하기 위해 1차원 팽창인과 합성곱(1D dilated causal convolution)을 사용한다. 먼저, WaveNet이 부호화된 정수 값이 아니라 실수 값을 출력하여 전력 데이터를 예측하기 적합하도록 개선하였다. 다음으로 학습 과정에 적용된 하이퍼파라미터(입력 기간, 배치 크기, WaveNet 블록 개수, 팽창 비율, 학습률 변경)를 조정하여 적절한 성능을 나타내도록 하였다. 마지막으로 성능 평가를 통해 전통적인 홀드아웃 검증 기법보다 본 연구에서 사용한 워크포워드 검증 기법이 전력 소비량 데이터 예측에 우수함 성능을 나타냄을 확인하였다.

• 수산해양교육연구
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• 제25권3호
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• pp.724-732
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• 2013

The purpose of this study is to obtain a basic data on layout of the trawlers' bridge equipment. The task activities of bridge workers involved in the navigation and fishing operation were analyzed by link analysis methods. The results are as follows. It was found that the movement pattern and frequency of bridge workers are different accordance with the bridge work (navigation, casting net, towing net and hauling net). The central workstation of movement of the bridge workers was a radar workstation, a steering workstation and a trawl winch workstation in the bridge work. But the radar did not show up as the center of movement during the hauling net. Workstations related deeply to the central workstations of the movement on the bridge were as below. Radar workstation was related to a GPS plotter, a microphone location for external communication with VHF and MF/HF equipment and a steering in the case of the navigation, the steering, the GPS plotter and the net monitor in the case of the fishing operation. Steering workstation was related deeply to the GPS plotter, the radar in the case of the navigation, a speed controller, the GPS plotter, a fish finder, the net monitor and the microphone location in the case of the fishing operation. Trawl winch workstation showed deep relation with the GPS plotter and the speed control during the fishing operation. Through this study, it was found that Workstations related deeply to the central workstation of the movement of the bridge workers in accordance with the bridge work. The results of this study might be utilized as the basic data on the bridge layout to minimize the fatigue degree due to a physical movement of the bridge workers.

• 전자공학회논문지B
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• 제33B권12호
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• pp.84-94
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• 1996

In this work, a fuzzy petri net model for modeling a general form of fuzzy production system which consists of chaining fuzzy production rules and so requires multistage reasoning process is presented. For the obtained fuzzy petri net model, the net will be transformed into some matrices, and also be systematically led to an algebraic form of a state equation. Since it is fond that the approximate reasoning process in fuzzy systems corresponds to the dynamic behavior of the fuzzy petri net, it is further shown that the multistage reasoning process can be carried out by executing the state equation.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4751-4758
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• 2022

Two-phase flow may almost exist in every branch of the energy industry. For the corresponding engineering design, it is very essential and crucial to monitor flow patterns and their transitions accurately. With the high-speed development and success of deep learning based on convolutional neural network (CNN), the study of flow pattern identification recently almost focused on this methodology. Additionally, the photographing technique has attractive implementation features as well, since it is normally considerably less expensive than other techniques. The development of such a two-phase flow pattern online monitoring system is the objective of this work, which seldom studied before. The ongoing preliminary engineering design (including hardware and software) of the system are introduced. The flow pattern identification method based on CNNs and transfer learning was discussed in detail. Several potential CNN candidates such as ALexNet, VggNet16 and ResNets were introduced and compared with each other based on a flow pattern dataset. According to the results, ResNet50 is the most promising CNN network for the system owing to its high precision, fast classification and strong robustness. This work can be a reference for the online monitoring system design in the energy system.

• 월간포장계
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• 통권110호
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• pp.186-203
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• 2002

• 월간포장계
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• 통권148호
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• pp.156-181
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• 2005

• 월간포장계
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• 통권223호
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• pp.120-155
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• 2011