• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.16 no.1
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• pp.27-40
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• 1998

This paper addresses the suitability of GPS positioning technology to monitoring deformation and movement of structures. The first part of the study is an empirical quantitative study of the repeatability of GPS observations and the second part is a performance evaluation of kinematic GPS, which requires only a few minutes per a point, for monitoring deformation of an engineering structure. On the test network for monitoring of a earth am, four observations have been conducted repeatedly on different seasons and water levels. The reference network was observed in static mode, and monitoring points were observed respectively in rapid-static mode as well as in kinematic mode in each epoch and then the results were compared with those obtained by conventional surveying techniques. The repeatability of baseline vectors to better than average 7 mm in three dimensions was achieved in base line observations between reference points and also the unclosure of reference networks showed the range of 4 ppm to 27 ppm. Compared with conventional surveying techniques, the kinematic approach showed the differences of 3∼4 m in slope distances which were observed from reference points to monitoring points, and showed the differences of 4∼8 m in height. It was ascertained that the kinematic GPS technology provides an efficient alternative to deformation monitoring by conventional means which are capable of detecting movements in the order of 5 mm.

• Journal of Korea Society of Industrial Information Systems
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• v.16 no.2
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• pp.85-97
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• 2011

Wireless sensor networks have emerged as one of the key enabling technologies for ubiquitous computing since wireless intelligent sensor nodes connected by short range communication media serve as a smart intermediary between physical objects and people in ubiquitous computing environment. We recognize the wireless sensor network as a massively distributed and deeply embedded system. Such systems require concurrent and asynchronous event handling as a distributed system and resource-consciousness as an embedded system. Since the operating environment and architecture of wireless sensor networks, with the seemingly conflicting requirements, poses unique design challenges and constraints to developers, we propose a very new operating system for sensor nodes based on finite state machine. In this paper, we clarify the design goals reflected from the characteristics of sensor networks, and then present the heart of the design and implementation of a compact and efficient state-driven operating system, SenOS. We describe how SenOS can operate in an extremely resource constrained sensor node while providing the required reactivity and dynamic reconfigurability with low update cost. We also compare our experimental results after executing some benchmark programs on SenOS with those on TinyOS.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.441-446
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• 2020

In this paper, we propose the α-Feature Map Scaling (α-FMS) method which extends the FMS method that was designed to enhance the discriminative power of feature maps of deep neural networks in Speaker Verification (SV) systems. The FMS derives a scale vector from a feature map and then adds or multiplies them to the features, or sequentially apply both operations. However, the FMS method not only uses an identical scale vector for both addition and multiplication, but also has a limitation that it can only add a value between zero and one in case of addition. In this study, to overcome these limitations, we propose α-FMS to add a trainable parameter α to the feature map element-wise, and then multiply a scale vector. We compare the performance of the two methods: the one where α is a scalar, and the other where it is a vector. Both α-FMS methods are applied after each residual block of the deep neural network. The proposed system using the α-FMS methods are trained using the RawNet2 and tested using the VoxCeleb1 evaluation set. The result demonstrates an equal error rate of 2.47 % and 2.31 % for the two α-FMS methods respectively.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.11
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• pp.11-18
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• 2014

Most of the energy efficient MAC protocols for wireless sensor networks (WSNs) are based on duty cycling in a single channel and show competitive performances in a small number of traffic flows; however, under concurrent multiple flows, they result in significant performance degradation due to contention and collision. We propose a multi-channel pipelining (MCP) method for convergecast WSN in order to address these problems. In MCP, a staggered dynamic phase shift (SDPS) algorithms devised to minimize end-to-end latency by dynamically staggering wake-up schedule of nodes on a multi-hop path. Also, a phase-locking identification (PLI) algorithm is proposed to optimize energy efficiency. Based on these algorithms, multiple flows can be dynamically pipelined in one of multiple channels and successively handled by sink switched to each channel. We present an analytical model to compute the duty cycle and the latency of MCP and validate the model by simulation. Simulation evaluation shows that our proposal is superior to existing protocols: X-MAC and DPS-MAC in terms of duty cycle, end-to-end latency, delivery ratio, and aggregate throughput.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.6-16
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• 2008

An instable wireless channel condition causes more packet losses and retransmissions due to interference, fading, station mobility, and so on. Therefore video streaming service over a wireless networks is a challenging task because of the changes in the wireless channel conditions and time-constraints characteristics of the video streaming services. To provide efficient video streaming over a wireless networks, QoS-enhanced MAC protocol, IEEE 802.11e, is standardized recently. Tn this paper, we propose a new network-adaptive H.264 video streaming mechanism in the IEEE 802.11e wireless networks. To improve the quality of video streaming services, video stream has to adapt to the changes in the wireless channel conditions. The wireless channel conditions are estimated by the packet loss probability and informed to the application layer by the cross-layering. According to the wireless channel information, the video streaming application filters out the low-priority data. This adaptation mechanism efficiently uses system resources because it drops the low-priority data in advance. Therefore, our cross-layer design can provide improved video streaming services to the end-user. Through the implementation and performance evaluation, we prove that the proposed mechanism improves the QoS of the video streaming by providing the smoothed playback.

• Journal of the Korea Convergence Society
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• v.12 no.9
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• pp.11-19
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• 2021

A software system is required to change during its life cycle due to various requirements such as adding functionalities, fixing bugs, and adjusting to new computing environments. Such program code modification should be considered as carefully as a new system development becase unexpected software errors could be introduced. In addition, when reusing open source programs, we can expect higher quality software if code changes of the open source program are predicted in advance. This paper proposes a Convolutional Neural Network (CNN)-based deep learning model to predict source code changes. In this paper, the prediction of code changes is considered as a kind of a binary classification problem in deep learning and labeled datasets are used for supervised learning. Java projects and code change logs are collected from GitHub for training and testing datasets. Software metrics are computed from the collected Java source code and they are used as input data for the proposed model to detect code changes. The performance of the proposed model has been measured by using evaluation metrics such as precision, recall, F1-score, and accuracy. The experimental results show the proposed CNN model has achieved 95% in terms of F1-Score and outperformed the multilayer percept-based DNN model whose F1-Score is 92%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of Korea Society of Industrial Information Systems
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• v.25 no.6
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• pp.33-45
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• 2020

This paper aims to look at the perspective that the latest cutting-edge technologies are predicting individual diseases in the actual medical environment in a situation where various types of wearable devices are rapidly increasing and used in the healthcare domain. Through the process of collecting, processing, and transmitting data by merging clinical data, genetic data, and life log data through a user-participating wearable device, it presents the process of connecting the learning model and the feedback model in the environment of the Deep Neural Network. In the case of the actual field that has undergone clinical trial procedures of medical IT occurring in such a high-tech medical field, the effect of a specific gene caused by metabolic syndrome on the disease is measured, and clinical information and life log data are merged to process different heterogeneous data. That is, it proves the objective suitability and certainty of the deep neural network of heterogeneous data, and through this, the performance evaluation according to the noise in the actual deep learning environment is performed. In the case of the automatic encoder, we proved that the accuracy and predicted value varying per 1,000 EPOCH are linearly changed several times with the increasing value of the variable.

• Journal of The Korean Astronomical Society
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• v.56 no.2
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• pp.213-224
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• 2023

Type II solar radio bursts show frequency drifts from high to low over time. They have been known as a signature of coronal shock associated with Coronal Mass Ejections (CMEs) and/or flares, which cause an abrupt change in the space environment near the Earth (space weather). Therefore, early detection of type II bursts is important for forecasting of space weather. In this study, we develop a deep-learning (DL) model for the automatic detection of type II bursts. For this purpose, we adopted a 1-D Convolution Neutral Network (CNN) as it is well-suited for processing spatiotemporal information within the applied data set. We utilized a total of 286 radio burst spectrum images obtained by Hiraiso Radio Spectrograph (HiRAS) from 1991 and 2012, along with 231 spectrum images without the bursts from 2009 to 2015, to recognizes type II bursts. The burst types were labeled manually according to their spectra features in an answer table. Subsequently, we applied the 1-D CNN technique to the spectrum images using two filter windows with different size along time axis. To develop the DL model, we randomly selected 412 spectrum images (80%) for training and validation. The train history shows that both train and validation losses drop rapidly, while train and validation accuracies increased within approximately 100 epoches. For evaluation of the model's performance, we used 105 test images (20%) and employed a contingence table. It is found that false alarm ratio (FAR) and critical success index (CSI) were 0.14 and 0.83, respectively. Furthermore, we confirmed above result by adopting five-fold cross-validation method, in which we re-sampled five groups randomly. The estimated mean FAR and CSI of the five groups were 0.05 and 0.87, respectively. For experimental purposes, we applied our proposed model to 85 HiRAS type II radio bursts listed in the NGDC catalogue from 2009 to 2016 and 184 quiet (no bursts) spectrum images before and after the type II bursts. As a result, our model successfully detected 79 events (93%) of type II events. This results demonstrates, for the first time, that the 1-D CNN algorithm is useful for detecting type II bursts.