• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.454-458
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• 2008

This article describes an interference reduction scheme for Mobile WiMAX in an indoor environment. The feasibility of user deployed femtocells in the same frequency channel as an existing macro cell network is investigated. One of the important requirements for co-channel operation of femtocells such as auto-configuration and self optimization are discussed. In femtocell deployments, leakage of the pilot signal to the outside of a house can result of the higher number of mobility events caused by passing user of macrocell. This interference effect can be minimized by reducing the pilot power using proper scheme. This paper introduces existing auto-configuration method of power control and proposed interference reduction scheme using power control for Mobile WiMAX in an indoor environment.

• Journal of the Korea Society for Simulation
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• v.20 no.1
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• pp.107-116
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• 2011

Recently, femtocell has been concerned as one of effective solutions to relieve shadow region and provide high quality services to users in indoor environments. Even though femtocell offers various benefits to cellular operators and users, many technical issues, such as interference coordination, network synchronization, self-configuration, self-optimization, and so on, should be solved to deploy the femtocell in current network. In this paper, we develop a simulator for evaluating performance of long term evolution (LTE) femtocell systems under various interference scenarios. The simulator consists of a main-module and five sub-modules. The main-module connects and manages five sub-modules which have the functionality managing user mobility, packet scheduling, call admission control, traffic generation, and modulation and coding scheme (MCS). To provide user convenience, the simulator adopts graphical user interface (GUI) which can observes simulation results in real time. We expect that this simulator can contribute to developing effective femtocell systems by supporting a tool for analyzing the effect of interference between macrocell and femtocell.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.1-15
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• 2024

The construction of smart communities is a new method and important measure to ensure the security of residential areas. In order to solve the problem of low accuracy in face recognition caused by distorting facial features due to monitoring camera angles and other external factors, this paper proposes the following optimization strategies in designing a face recognition network: firstly, a global graph convolution module is designed to encode facial features as graph nodes, and a multi-scale feature enhancement residual module is designed to extract facial keypoint features in conjunction with the global graph convolution module. Secondly, after obtaining facial keypoints, they are constructed as a directed graph structure, and graph attention mechanisms are used to enhance the representation power of graph features. Finally, tensor computations are performed on the graph features of two faces, and the aggregated features are extracted and discriminated by a fully connected layer to determine whether the individuals' identities are the same. Through various experimental tests, the network designed in this paper achieves an AUC index of 85.65% for facial keypoint localization on the 300W public dataset and 88.92% on a self-built dataset. In terms of face recognition accuracy, the proposed network achieves an accuracy of 83.41% on the IBUG public dataset and 96.74% on a self-built dataset. Experimental results demonstrate that the network designed in this paper exhibits high detection and recognition accuracy for faces in surveillance videos.

• Journal of Periodontal and Implant Science
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• v.48 no.2
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• pp.114-123
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• 2018

Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. Results: The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. Conclusions: We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.9
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• pp.764-774
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• 2013

The number of users and data packets has increased in 4G cellular networks. Therefore, 4G cellular network providers suffer from the network capacity problem. In order to solve this problem, femtocell concept is suggested. It can reduce the coverage hole and enhance the QoS. However, only small number of femtocells experience the large amount of loads. To solve this problem, Mobility Load Balancing (MLB) algorithm is suggested, which is a kind of load balancing algorithm. To distribute the traffic load, MLB algorithm modifies the handover region. If the handover region is reduced by MLB algorithm, some cell edge users are compulsively handed over to neighbor femtocell. In this paper, we analyze the relation between MLB performing period and performance indicators. For example throughput and blocking probability is reduced, if period is decreased. On the contrast, if period is increased, the number of handover frequency is decreased. Using this relation, we suggest the adaptive periodic MLB algorithm. This algorithm includes the advantage of both long period and short period MLB algorithm, such as high throughput, the small number of handover frequency, and low blocking probability.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.111-118
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• 2021

This paper proposes a novel method of caching contents that can be encoded into multiple quality levels in device-to-device (D2D)-assisted caching networks. Different from the existing caching schemes, the author allows caching fractions of an individual file and considers the self cache hit event, which the user can find the desired content in its device. The author analyzes the tradeoff between the quality of cached contents and the cache hit rate, and proposes the device caching method maximizing the expected quality that the user can enjoy. Depending on the parameter of the relationship between the quality and the file size, the optimal caching method can be obtained by solving the convex optimization problem and the DC programming problem. If the file size increases faster than the quality, the cached fractions of the contents continuously increase as the popularity grows. Meanwhile, if the file size increases slower than the quality, some of the high-popularity files are entirely cached but others are not cached at all.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.7 no.2
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• pp.132-137
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• 2007

Experimental software data capturing the essence of software projects (expressed e.g., in terms of their complexity and development time) have been a subject of intensive modeling. In this study, we introduce a new category of Hybrid Fuzzy Neural Networks (gHFNN) and discuss their comprehensive design methodology. The gHFNN architecture results from highly synergistic linkages between Fuzzy Neural Networks (FNN) and Polynomial Neural Networks (PNN). We develop a rule-based model consisting of a number of "if-then" statements whose antecedents are formed in the input space and linked with the consequents (conclusion pats) formed in the output space. In this framework, FNNs contribute to the formation of the premise part of the overall network structure of the gHFNN. The consequences of the rules are designed with the aid of genetically endowed PNNs. The experiments reported in this study deal with well-known software data such as the NASA dataset. In comparison with the previously discussed approaches, the proposed self-organizing networks are more accurate and yield significant generalization abilities.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.2
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• pp.119-127
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• 2004

Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur. resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, the monitoring of cavitation is of economic interest and is very importance in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals that are acquired from butterfly valves in the pumping stations and compared the classification success rate with those of self-organizing feature map neural network.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.3
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• pp.157-163
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• 2014

This paper demonstrates the benefit of using MOOS-IvP in the development of control system for Unmanned Underwater Vehicles(UUV). The demand for autonomy in UUVs has significantly increased due to the complexity in missions to be performed. Furthermore, the increased number of sensors and actuators that are interconnected through a network has introduced a need for a middleware platform for UUVs. In this context, MOOS-IvP, which is an open source software architecture, has been developed by several researchers from MIT, Oxford University, and NUWC. The MOOS software is a communication middleware based on the publish-subscribe architecture allowing each application to communicate through a MOOS database. The IvP Helm, which is one of the MOOS modules, publishes vehicle commands using multi-objective optimization in order to implement autonomous decision making. This paper explores the benefit of MOOS-IvP in the development of control software for UUVs by using a case study with an auto depth control system based on self-organizing fuzzy logic control. The simulation results show that the design and verification of UUV control software based on MOOS-IvP can be carried out quickly and efficiently thanks to the reuse of source codes, modular-based architecture, and the high level of scalability.