• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.2
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• pp.542-558
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• 2016

Vehicular Ad Hoc Networks (VANETs) are self-configuring networks where the nodes are vehicles equipped with wireless communication technologies. In such networks, limitation of signal coverage and fast topology changes impose difficulties to the proper functioning of the routing protocols. Traditional Mobile Ad Hoc Networks (MANET) routing protocols lose their performance, when communicating between vehicles, compromising information exchange. Obviously, most applications critically rely on routing protocols. Thus, in this work, we propose a methodology for investigating the performance of well-established protocols for MANETs in the VANET arena and, at the same time, we introduce a routing protocol, called Genetic Network Protocol (G-NET). It is based in part on Dynamic Source Routing Protocol (DSR) and on the use of Genetic Algorithms (GAs) for maintenance and route optimization. As G-NET update routes periodically, this work investigates its performance compared to DSR and Ad Hoc on demand Distance Vector (AODV). For more realistic simulation of vehicle movement in urban environments, an analysis was performed by using the VanetMobiSim mobility generator and the Network Simulator (NS-3). Experiments were conducted with different number of vehicles and the results show that, despite the increased routing overhead with respect to DSR, G-NET is better than AODV and provides comparable data delivery rate to the other protocols in the analyzed scenarios.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.136-141
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• 2022

A very complex task in deep learning such as image classification must be solved with the help of neural networks and activation functions. The backpropagation algorithm advances backward from the output layer towards the input layer, the gradients often get smaller and smaller and approach zero which eventually leaves the weights of the initial or lower layers nearly unchanged, as a result, the gradient descent never converges to the optimum. We propose a two-factor non-saturating activation functions known as Bea-Mish for machine learning applications in deep neural networks. Our method uses two factors, beta (𝛽) and alpha (𝛼), to normalize the area below the boundary in the Mish activation function and we regard these elements as Bea. Bea-Mish provide a clear understanding of the behaviors and conditions governing this regularization term can lead to a more principled approach for constructing better performing activation functions. We evaluate Bea-Mish results against Mish and Swish activation functions in various models and data sets. Empirical results show that our approach (Bea-Mish) outperforms native Mish using SqueezeNet backbone with an average precision (AP50val) of 2.51% in CIFAR-10 and top-1accuracy in ResNet-50 on ImageNet-1k. shows an improvement of 1.20%.

• Spatial Information Research
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• v.12 no.2
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• pp.165-179
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• 2004

With the spread of wireless internet, the interest in mobile applications and services is increasing. Korea Wireless Internet Standardization Forum has been establishing the standards for mobile platform and map service in the wireless internet environment. This study aims to present a paragon of mapping middleware that plays the role of broker for wireless internet map service: in particular, it focuses on the interoperability with generic map servers. In this study, we developed a method for applying current map servers to the wireless internet map service, and analyzed the request/response structure of the map servers which have different operation characteristics in order to allow our middleware to fully utilize the functionalities of the map servers. The middleware we developed is composed of .NET-based XML Web Services: it has a lightweight module for image map and a map representation module for choropleth map, symbol map, chart map, etc. This mapping middleware is a broker between mobile client and generic map server, and supports .NET clients and Java clients as well. Its component-based interoperability grants the extensibility for the wireless internet dedicated map servers of the future in addition to the current generic map servers.

• Electronics and Telecommunications Trends
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• v.15 no.6 s.66
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• pp.9-17
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• 2000

본 고에서는 현재 전세계적으로 진행되고 있는 차세대인터넷 망 구축 동향을 소개한다. 한국의 KOREN IPv6 프로젝트를 포함한 미국의 Internet2 백본인 vBNS, 유럽의 IPv6 프로젝트인 6INIT, 캐나다의 광 인터넷 백본인 CANARIE의 CA*net 프로젝트 그리고 일본의 WIDE 프로젝트에서 수행중인 IPv6 기반 차세대 인터넷 망 구축현황에 대해 소개한다.

• Journal of Advanced Navigation Technology
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• v.13 no.1
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• pp.68-73
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• 2009

Recently, various services through the Internet are gradually increased and developed. The traditional way of examination has been replacing by the online test as well. The most representative is the IBT TOEFL by the ETS in the US. Besides this, a lot of online tests and some related discussions are very fierce to carry out due to the continuous increase of the number of exam candidates. Taking account of the economic issues compared to the previous test, this online method has a lot of strengths. This paper aims to build an online test system based on the 3- tier browser-server architecture, which is different from the commonly used 2-tier based system. This system was achieved using the Visual Studio.Net 2005 and SQL Server 2000 as development tools, and based on the ASP.NET 2.0 platform, using the ADO.NET and C# language.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.306-310
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• 2002

Today, corporations are approach to inside information using public network such as Internet groping economical way to utilize public network to corporation information network without buying equipment. But, special quality of internet, Virtual Private Net (VPN) that it uses net that is observed as technology that can be guaranteed public safety division transmission and data securitybecause of can not secure data transmission. In this paper. add VPN function ONU&OLT of ATM-PON system and propose SCB (Single Copy Broadcasting). When there is VPN function to ATM network, the speed can be fast, and reduce rain track pick quantity during time more. Performance analysis network simulation that use NS-2.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4426-4442
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• 2020

Since scene segmentation is becoming a hot topic in the field of autonomous driving and medical image analysis, researchers are actively trying new methods to improve segmentation accuracy. At present, the main issues in image semantic segmentation are intra-class inconsistency and inter-class indistinction. From our analysis, the lack of global information as well as macroscopic discrimination on the object are the two main reasons. In this paper, we propose a Densely connected residual Attention network (DA-Res2Net) which consists of a dense residual network and channel attention guidance module to deal with these problems and improve the accuracy of image segmentation. Specifically, in order to make the extracted features equipped with stronger multi-scale characteristics, a densely connected residual network is proposed as a feature extractor. Furthermore, to improve the representativeness of each channel feature, we design a Channel-Attention-Guide module to make the model focusing on the high-level semantic features and low-level location features simultaneously. Experimental results show that the method achieves significant performance on various datasets. Compared to other state-of-the-art methods, the proposed method reaches the mean IOU accuracy of 83.2% on PASCAL VOC 2012 and 79.7% on Cityscapes dataset, respectively.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.406-412
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• 2022

Scene classification of very high-resolution (VHR) imagery can attribute semantics to land cover in a variety of domains. Real-world application requirements have not been addressed by conventional techniques for remote sensing image classification. Recent research has demonstrated that deep convolutional neural networks (CNNs) are effective at extracting features due to their strong feature extraction capabilities. In order to improve classification performance, these approaches rely primarily on semantic information. Since the abstract and global semantic information makes it difficult for the network to correctly classify scene images with similar structures and high interclass similarity, it achieves a low classification accuracy. We propose a VHR remote sensing image classification model that uses extracts the global feature from the original VHR image using an EfficientNet-V2L CNN pre-trained to detect similar classes. The image is then classified using a multilayer perceptron (MLP). This method was evaluated using two benchmark remote sensing datasets: the 21-class UC Merced, and the 38-class PatternNet. As compared to other state-of-the-art models, the proposed model significantly improves performance.

• Journal of Internet Computing and Services
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• v.8 no.3
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• pp.19-32
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• 2007

This paper present a new face recognition method based on LVQ neural net to construct a real time face recognition system. The previous researches which used PCA, LDA combined neural net usually need much time in training neural net. The supervised LVQ neural net needs much less time in training and can maximize the separability between the classes. In this paper, the proposed method transforms the input face image by PCA and LDA sequentially into low-dimension feature vectors and recognizes the face through LVQ neural net. In order to make the system robust to external light variation, light compensation is performed on the detected face by max-min normalization method as preprocessing. PCA and LDA transformations are applied to the normalized face image to produce low-level feature vectors of the image. In order to determine the initial centers of LVQ and speed up the convergency of the LVQ neural net, the K-Means clustering algorithm is adopted. Subsequently, the class representative vectors can be produced by LVQ2 training using initial center vectors. The face recognition is achieved by using the euclidean distance measure between the center vector of classes and the feature vector of input image. From the experiments, we can prove that the proposed method is more effective in the recognition ratio for the cases of still images from ORL database and sequential images rather than using conventional PCA of a hybrid method with PCA and LDA.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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• pp.83-92
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• 2020

A recent study to determine the fall is focused on analyzing fall motions using a recurrent neural network (RNN), and uses a deep learning approach to get good results for detecting human poses in 2D from a mono color image. In this paper, we investigated the improved detection method to estimate the position of the head and shoulder key points and the acceleration of position change using the skeletal key points information extracted using PoseNet from the image obtained from the 2D RGB low-cost camera, and to increase the accuracy of the fall judgment. In particular, we propose a fall detection method based on the characteristics of post-fall posture in the fall motion analysis method and on the velocity of human body skeleton key points change as well as the ratio change of body bounding box's width and height. The public data set was used to extract human skeletal features and to train deep learning, GRU, and as a result of an experiment to find a feature extraction method that can achieve high classification accuracy, the proposed method showed a 99.8% success rate in detecting falls more effectively than the conventional primitive skeletal data use method.