• Journal of Advanced Navigation Technology
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• v.13 no.3
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• pp.351-357
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• 2009

In this paper, we studied an efficient inter-working method in which QualNet network simulator can import WAVE channel model and physical layer simulation module pre-designed by Matlab tool. At first, we investigated physical layer and communication medium simply designed in QualNet, then we suggested practical method for QualNet network simulator to adopt different type of physical layer simulation module in which detailed multi-path fading channel model and IEEE802.11p communication modem are designed. This work should be applied to linked simulation between upper layer and lower physical layer for total simulation from higher layer to lower physical layer related to next generation DSRC/WAVE specification.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.5
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• pp.313-322
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• 2020

A large volume of data will improve the robustness of deep learning models and avoid overfitting problems. In automatic tongue segmentation, the availability of annotated tongue images is often limited because of the difficulty of collecting and labeling the tongue image datasets in reality. Data augmentation can expand the training dataset and increase the diversity of training data by using label-preserving transformations without collecting new data. In this paper, augmented tongue image datasets were developed using seven augmentation techniques such as image cropping, rotation, flipping, color transformations. Performance of the data augmentation techniques were studied using state-of-the-art transfer learning models, for instance, InceptionV3, EfficientNet, ResNet, DenseNet and etc. Our results show that geometric transformations can lead to more performance gains than color transformations and the segmentation accuracy can be increased by 5% to 20% compared with no augmentation. Furthermore, a random linear combination of geometric and color transformations augmentation dataset gives the superior segmentation performance than all other datasets and results in a better accuracy of 94.98% with InceptionV3 models.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.67-77
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• 2023

This study represents an innovative research conducted in the smart farm environment, developing a deep learning-based disease and pest detection model and applying it to the Intelligent Internet of Things (IoT) platform to explore new possibilities in the implementation of digital agricultural environments. The core of the research was the integration of the latest ImageNet models such as Pseudo-Labeling, RegNet, EfficientNet, and preprocessing methods to detect various diseases and pests in complex agricultural environments with high accuracy. To this end, ensemble learning techniques were applied to maximize the accuracy and stability of the model, and the model was evaluated using various performance indicators such as mean Average Precision (mAP), precision, recall, accuracy, and box loss. Additionally, the SHAP framework was utilized to gain a deeper understanding of the model's prediction criteria, making the decision-making process more transparent. This analysis provided significant insights into how the model considers various variables to detect diseases and pests.

• Korean Journal of Computational Design and Engineering
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• v.9 no.3
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• pp.203-211
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• 2004

In this paper, we present a method of extracting a regular 2-manifold triangular net from a triangular net including degenerate and self-intersected triangles. This method can be applied to obtaining an offset model without degenerate and self-intersected triangles. Then this offset model can be used to generate CL curves and extract machining features for CAPP The robust and efficient algorithm to detect valid triangles by growing regions from an initial valid triangle is presented. The main advantage of the algorithm is that detection of valid triangles is performed only in valid regions and their adjacent selfintersections, and omitted in the rest regions (invalid regions). This advantage increases robustness of the algorithm. As well as a k-d tree bucketing method is used to detect self-intersections efficiently.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.39 no.6
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• pp.19-25
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• 2002

A regular virtual topology requires little processing time for routing purposes which may be a desirable property for high-speed networks. Asymmetric bilayered ShuffleNet, proposed by us as a virtual topology, can be more efficient to be used to design a wide-area optical network compared to ShuffleNet. In this paper, asymmetric bilayered ShuffleNet is imbedded on a given physical topology with the objective of minimizing the total message delay.

• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.20-27
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• 2006

In this paper we constructed semantic net that can efficiently conform retrieval and reuse of object-oriented source code. In order that initial relevance of semantic net was constructed using thesaurus to represent concept of object-oriented inheritance between each node. Also we made up for the weak points in spreading activation method that use to activate node and line of semantic net and to impulse activation value. Therefore we proposed the method to enhance retrieval time and to keep the quality of spreading activation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4026-4040
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• 2019

In this paper, we present an approach for detecting slide transitions in lecture videos by introducing the spatio-temporal residual networks. Given a lecture video which records the digital slides, the speaker, and the audience by multiple cameras, our goal is to find keyframes where slide content changes. Since temporal dependency among video frames is important for detecting slide changes, 3D Convolutional Networks has been regarded as an efficient approach to learn the spatio-temporal features in videos. However, 3D ConvNet will cost much training time and need lots of memory. Hence, we utilize ResNet to ease the training of network, which is easy to optimize. Consequently, we present a novel ConvNet architecture based on 3D ConvNet and ResNet for slide transition detection in lecture videos. Experimental results show that the proposed novel ConvNet architecture achieves the better accuracy than other slide progression detection approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.1
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• pp.1-8
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• 2018

Recently, the convolutional neural network shows high performance in many computer vision tasks. However, convolutional neural networks require enormous amount of operation, so it is difficult to adopt them in the embedded environments. To solve this problem, many studies are performed on the ASIC or FPGA implementation, where an efficient representation method is required. The fixed-point representation is adequate for the ASIC or FPGA implementation but causes a performance degradation. This paper proposes a separate optimization of representations for the convolutional layers and the batch normalization layers. With the proposed method, the required bit width for the convolutional layers is reduced from 16 bits to 10 bits for the ResNet-50 neural network. Since the computation amount of the convolutional layers occupies the most of the entire computation, the bit width reduction in the convolutional layers enables the efficient implementation of the convolutional neural networks.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.42 no.5 s.335
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• pp.29-38
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• 2005

In this paper, a new modified and optimized AntNet algorithm which can be implemented efficiently onto network processor is proposed. The AntNet that mimics the activities of the social insect is an adaptive agent-based routing algorithm. This method requires a complex arithmetic calculating system. However, since network processors have simple arithmetic units for a packet processing, it is very difficult to implement the original AntNet algorithm on network processors. Therefore, the proposed AntNet algorithm is a solution of this problem by decreasing arithmetic executing cycles for calculating a reinforcement value without loss of the adaptive performance. The results of the simulations show that the proposed algorithm is more suitable and efficient than the original AntNet algorithm for commercial network processors.

• Korean Journal of Ecology and Environment
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• v.53 no.2
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• pp.156-164
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• 2020

The accurate estimation of fish assemblages is highly dependent on the sampling gear used for sampling. We used data from 15 sampling sites along the Nakdong River, which is a large river in South Korea, to identify differences in assemblages and sizes of freshwater fishes collected with either cast nets or gill nets, the two most commonly used sampling gear in South Korea. The two gears differed in the fish assemblages they captured, with more species caught by gill nets. Further, due to its tighter mesh size, the cast net caught significantly smaller fishes than the gill nets(independent t-test, p<0.05). We found the cast net to be appropriate for species that inhabit shallow (less than 2 m) and open water, but inappropriate for deep water, habitats with plant beds, and nocturnal species. Thus, cast net sampling is not efficient in a large river environment, and a combination of sampling methods is more suitable for understanding fish assemblages in such habitats. In general, appropriate selection of fishing methods to specific habitats is necessary to improve data quality and minimize the misrepresentation of environmental conditions.