• International Journal of Computer Science & Network Security
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• v.23 no.7
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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.

• Journal of the Korea Society of Computer and Information
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• v.4 no.3
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• pp.13-19
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• 1999

In this paper, we suggests a WIP (Work In Process) of FMC (Flexible Manufacturing Cell) analysis methods based on the TPN (Time Petri Nets) unfolding. Unfolding of PN is a partial order-based method for the verification of concurrent system without the state space explosion. The aim of this work is to formulate the general cyclic state scheduling problem to minimize the WIP to satisfy economical constraints. The method is based on unfolding of the original net into the equivalent acyclic description.

• Journal of the Korean Wood Science and Technology
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• v.13 no.4
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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.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Journal of Fisheries and Marine Sciences Education
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• v.25 no.3
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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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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• v.54 no.12
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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.

• The monthly packaging world
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• s.110
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• pp.186-203
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• 2002

• The monthly packaging world
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• s.148
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• pp.156-181
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• 2005

• The monthly packaging world
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• s.223
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• pp.120-155
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• 2011