• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.1
• /
• pp.23-33
• /
• 2020

Recently, high-resolution images can be easily acquired using UAV (Unmanned Aerial Vehicle), so that it is possible to produce small area observation and spatial information at low cost. In particular, research on the generation of cover maps in crop production areas is being actively conducted for monitoring the agricultural environment. As a result of comparing classification performance by applying RF(Random Forest), SVM(Support Vector Machine) and CNN(Convolutional Neural Network), deep learning classification method has many advantages in image classification. In particular, land cover classification using satellite images has the advantage of accuracy and time of classification using satellite image data set and pre-trained parameters. However, UAV images have different characteristics such as satellite images and spatial resolution, which makes it difficult to apply them. In order to solve this problem, we conducted a study on the application of deep learning algorithms that can be used for analyzing agricultural lands where UAV data sets and small-scale composite cover exist in Korea. In this study, we applied DeepLab V3 +, FC-DenseNet (Fully Convolutional DenseNets) and FRRN-B (Full-Resolution Residual Networks), the semantic image classification of the state-of-art algorithm, to UAV data set. As a result, DeepLab V3 + and FC-DenseNet have an overall accuracy of 97% and a Kappa coefficient of 0.92, which is higher than the conventional classification. The applicability of the cover classification using UAV images of small areas is shown.

• Journal of IKEEE
• /
• v.13 no.2
• /
• pp.108-117
• /
• 2009

In recent years, one of alternatives for constructing RFID networks that provide mobile services is using wireless sensor networks (WSN) to enhance network capacity, utility and scalability. Due to absence of compatible reader anti-collision control and channel capture phenomenon, the medium access control protocols as used in the RFID networks lead to reader collision and starvation problem. In this paper, we develop a MAC protocol which is called Enhanced Collision Avoidance MAC (ECO) to avoid reader to reader collisions in an integrated RFID network. ECO is a CSMA-based MAC protocol, and operates on integrated nodes which consist of a RFID reader and a mote. Performance evaluation shows superior results to pure-CSMA protocols under dense deployment environments, both in number of failures and in throughput.

• Proceedings of the KIEE Conference
• /
• 2007.04a
• /
• pp.433-435
• /
• 2007

Geographic wireless sensor networks use position information for Greedy routing. Greedy routing works well in dense network where as in sparse network it may fail and require the use of recovery algorithms. Recovery algorithms help the packet to get out of the communication void. However, these algorithms are generally costlier for resource constrained position based wireless sensor type networks. In the present work, we propose a Void Avoidance Algorithm (VAA); a novel idea based on virtual distance upgrading that allows wireless sensor nodes to remove all stuck nodes by transforming the routing graph and forward packet using greedy routing only without recovery algorithm. In VAA, the stuck node upgrades distance unless it finds next hop node which is closer to the destination than itself. VAA guarantees the packet delivery if there is a topologically valid path exists. NS-2 is used to evaluate the performance and correctness of VAA and compared the performance with GPSR. Simulation results show that our proposed algorithm achieves higher delivery ratio, lower energy consumption and efficient path.

• Journal of the Korean Wood Science and Technology
• /
• v.49 no.5
• /
• pp.491-503
• /
• 2021

Wood identification is regularly performed by observing the wood anatomy, such as colour, texture, fibre direction, and other characteristics. The manual process, however, could be time consuming, especially when identification work is required at high quantity. Considering this condition, a convolutional neural networks (CNN)-based program is applied to improve the image classification results. The research focuses on the algorithm accuracy and efficiency in dealing with the dataset limitations. For this, it is proposed to do the sample selection process or only take a small portion of the existing image. Still, it can be expected to represent the overall picture to maintain and improve the generalisation capabilities of the CNN method in the classification stages. The experiments yielded an incredible F1 score average up to 93.4% for medium sample area sizes (200 × 200 pixels) on each CNN architecture (VGG16, ResNet50, MobileNet, DenseNet121, and Xception based). Whereas DenseNet121-based architecture was found to be the best architecture in maintaining the generalisation of its model for each sample area size (100, 200, and 300 pixels). The experimental results showed that the proposed algorithm can be an accurate and reliable solution.

• Journal of Multimedia Information System
• /
• v.9 no.1
• /
• pp.21-32
• /
• 2022

Early-stage diagnosis of Alzheimer's Disease (AD) from Cognitively Normal (CN) patients is crucial because treatment at an early stage of AD can prevent further progress in the AD's severity in the future. Recently, computer-aided diagnosis using magnetic resonance image (MRI) has shown better performance in the classification of AD. However, these methods use a traditional machine learning algorithm that requires supervision and uses a combination of many complicated processes. In recent research, the performance of deep neural networks has outperformed the traditional machine learning algorithms. The ability to learn from the data and extract features on its own makes the neural networks less prone to errors. In this paper, a dense neural network is designed for binary classification of Alzheimer's disease. To create a classifier with better results, we studied result of different activation functions in the prediction. We obtained results from 5-folds validations with combinations of different activation functions and compared with each other, and the one with the best validation score is used to classify the test data. In this experiment, features used to train the model are obtained from the ADNI database after processing them using FreeSurfer software. For 5-folds validation, two groups: AD and CN are classified. The proposed DNN obtained better accuracy than the traditional machine learning algorithms and the compared previous studies for AD vs. CN, AD vs. Mild Cognitive Impairment (MCI), and MCI vs. CN classifications, respectively. This neural network is robust and better.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.3
• /
• pp.1014-1034
• /
• 2015

Location information of sensor nodes plays a critical role in many wireless sensor network (WSN) applications and protocols. Although many localization algorithms have been proposed in recent years, they usually target at dense networks and perform poorly in sparse networks. In this paper, we propose two component-based localization algorithms that can localize many more nodes in sparse networks than the state-of-the-art solution. We first develop the Basic Common nodes-based Localization Algorithm, namely BCLA, which uses both common nodes and measured distances between adjacent components to merge components. BCLA outperforms CALL, the state-of-the-art component-based localization algorithm that uses only distance measurements to merge components. In order to further improve the performance of BCLA, we further exploit the angular information among nodes to merge components, and propose the Component-based Localization with Angle and Distance information algorithm, namely CLAD. We prove the merging conditions for BCLA and CLAD, and evaluate their performance through extensive simulations. Simulations results show that, CLAD can locate more than 90 percent of nodes in a sparse network with average node degree 7.5, while CALL can locate only 78 percent of nodes in the same scenario.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.10
• /
• pp.4015-4033
• /
• 2015

The future mobile networks consist of hyper-dense heterogeneous and small cell networks of same or different radio access technologies (RAT). Integrating mobile networks of different RATs to provide seamless and smooth mobility service will be the target of future mobile converged network. Generally, handover from high-speed networks to low-speed networks faces many challenges from application perspective, such as abrupt bandwidth variation, packet loss, round trip time variation, connection disruption, and transmission blackout. Existing inter-RAT handover solutions cannot solve all the problems at the same time. Based on the high-layer convergence sublayer design, a new receiver-aided soft inter-RAT handover is proposed. This soft handover scheme takes advantage of multihoming ability of multi-mode mobile station (MS) to smooth handover procedure. In addition, handover procedure is seamless and applicable to frequent handover scenarios. The simulation results conducted in UMTS-WiMAX converged network scenario show that: in case of TCP traffics for handover from WiMAX to UMTS, not only handover latency and packet loss are eliminated completely, but also abrupt bandwidth/wireless RTT variation is smoothed. These delightful features make this soft handover scheme be a reasonable candidate of mobility management for future mobile converged networks.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.11
• /
• pp.2477-2487
• /
• 1997

In this paepr, we propose a dynamic multicast routin algorithm for constructing the delay-constrained minimal spanning tree in the VP-based ATM networks, in which we consider the effiiciency enen in the case wheree the destination dynamically joins/departs the multicast connection. For constructing the delay-constrained spanning tree, we frist generate a reduced network consisting of only VCX nodes from a given ATM network, originally consisting of VPX/VCX nodes. Then, we obtain the delay-constrained spanning tree with a minimal tree cost on the reduced network by using our proposed heuristic algorithm. Through numerical examples, we show that our dynamic multicast routing algorithm can provide an efficient usage of network resources when the membership nodes frequently changes during the lifetime of a multicast connection. We also demonstrate the more cost-saving can be expected in dense networks when applyingour proposed algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.189-190
• /
• 2009

For applications involving the monitoring of large areas, dense sensor networks are not practical. For such applications, delay tolerant networks which consist of disconnected clusters of sensors that are visited periodically by a mobile robot are implemented. Because clock synchronization is critical to any data collection endeavor, and because the structure of DTNs is unique, this paper examines various clock synchronization algorithms as they apply to DTNs. A simulation tool was developed to examine and evaluate the RBS clock synchronization algorithm for DTNs.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.05a
• /
• pp.652-654
• /
• 2023

The accurate prediction of User Equipment (UE) paths in wireless networks is crucial for improving handover mechanisms and optimizing network performance, particularly in the context of Beyond 5G and 6G networks. This paper presents a comprehensive evaluation of unidirectional and bidirectional recurrent neural network (RNN) architectures for UE path prediction. The study employs a sequence-to-sequence model designed to forecast user paths in a wireless network environment, comparing the performance of unidirectional and bidirectional RNNs. Through extensive experimentation, the paper highlights the strengths and weaknesses of each RNN architecture in terms of prediction accuracy and computational efficiency. These insights contribute to the development of more effective predictive path-based mobility management strategies, capable of addressing the challenges posed by ultra-dense cell deployments and complex network dynamics.