• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.5
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• pp.139-145
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• 2015

This paper proposes to increase the node energy effienciecy, which rapidly drops during the transmission of LEACH (Low Energy Adaptive Clustering Hierachy), using the method of DL-LEACH (Dual-hop Layered LEACH). By introducing dual-hop method in the data transmission, the proposed single-hop method for short-range transmission and multi-hop transmission method between the cluster heads for remote transmission was introduce. By introducing a partial multi-hop method in the data transmission, a single-hop method for short range transmission method between the cluster heads for remote transmission was introduces. In the proposed DL-LEACH, the energy consumption of cluster head for remote transmission reduces and increases the energy efficiency of sensor node by reducing the transmission distance and simplifying the transmission routine for short-range transmission. As compared the general LEACH, it was adapted to a wider sensor field.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.5
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• pp.209-214
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• 2014

IMS(IP Multi-Media Subsystem) is in the limelight as the Integrated wire and wireless Systems because of a sudden increase of smart mobile devices and growth of multimedia additional services such as IPTV. The structure of IMS is designed as a session control layer to provide various multimedia summative service using SIP based on IP communication network in order to carry out set-up, change and release by NGN of course, the existing voice services. But now It is broadly substituting in the IPTV, wire phone company and it is substituted in internet platform base on the soft-switch in currently. Especially, in currently, 4G LTE in a mobile communication company is rapidly growing in market. Therefore, in this study, we had designed and developed to the main prosser that can admit to 1000 user over and SIP gateway which can link the IMS Core that can link SIP Device which adopt the standard protocol on the SIP and to provide variable multimedia services.

• Bulletin of the Korean Chemical Society
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• v.35 no.10
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• pp.2974-2978
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• 2014

$Mn_3O_4$/multi-walled carbon nanotube (MWCNT) composites are prepared by chemically synthesizing $Mn_3O_4$ nanoparticles on a MWCNT film at room temperature. Structural and morphological characterization has been carried out using X-ray diffraction (XRD) and scanning and transmission electron microscopies (SEM and TEM). These reveal that polycrystalline $Mn_3O_4$ nanoparticles, with sizes of about 10-20 nm, aggregate to form larger nanoparticles (50-200 nm), and the $Mn_3O_4$ nanoparticles are attached inhomogeneously on MWCNTs. The electrochemical behavior of the composites is analyzed by cyclic voltammetry experiment. The $Mn_3O_4$/MWCNT composite exhibits a specific capacitance of $257Fg^{-1}$ at a scan rate of $5mVs^{-1}$, which is about 3.5 times higher than that of the pure $Mn_3O_4$. Cycle-life tests show that the specific capacitance of the $Mn_3O_4$/MWCNT composite is stable up to 1000 cycles with about 85% capacitance retention, which is better than the pure $Mn_3O_4$ electrode. The improved supercapacitive performance of the $Mn_3O_4$/MWCNT composite electrode can be attributed to the synergistic effects of the $Mn_3O_4$ nanoparticles and the MWCNTs, which arises not only from the combination of pseudocapacitance from $Mn_3O_4$ nanoparticles and electric double layer capacitance from the MWCNTs but also from the increased surface area, pore volume and conducting property of the MWCNT network.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.789-798
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• 2021

In this study, for the purpose of collecting and analyzing target-specific RCS data of target signals simulator for verification/improvement of radar system performance, VHF band monostatic/bistatic RCS of civil aircraft(B-747, B-737) and fighter(F-16) models were analyzed by EM simulation tool. In order to reduce the RCS analysis time, the analysis time and RCS data were compared and cross-verified. Also, the analysis range was selected by examining the interpolation error according to the analysis angle resolution. The RCS data obtained for each model were analyzed separately by the incident/reflection elevation angle and frequency. The RCS characteristics according to the shape of the aircraft and the incident/reflection azimuth angle were described. Finally, the statistical RCS distribution value of each model is presented through RCS distribution histogram analysis. In the future, the RCS database obtained by this study will be used for the target signals simulator of the VHF band radar system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.1
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• pp.62-77
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• 2010

Emerging applications with high data rates will need to transport bulk data reliably in wireless sensor networks. ARQ (Automatic Repeat request) or Forward Error Correction (FEC) code schemes can be used to provide reliable transmission in a sensor network. However, the naive ARQ approach drops the whole frame, even though there is a bit error in the frame and the FEC at the bit level scheme may require a highly complex method to adjust the amount of FEC redundancy. We propose a bulk data transmission scheme based on erasure-resilient code in this paper to overcome these inefficiencies. The sender fragments bulk data into many small blocks, encodes the blocks with LT codes and packages several such blocks into a frame. The receiver only drops the corrupted blocks (compared to the entire frame) and the original data can be reconstructed if sufficient error-free blocks are received. An incidental benefit is that the frame error rate (FER) becomes irrelevant to frame size (error recovery). A frame can therefore be sufficiently large to provide high utilization of the wireless channel bandwidth without sacrificing the effectiveness of error recovery. The scheme has been implemented as a new data link layer in TinyOS, and evaluated through experiments in a testbed of Zigbex motes. Results show single hop transmission throughput can be improved by at least 20% under typical wireless channel conditions. It also reduces the transmission time of a reasonable range of size files by more than 30%, compared to a frame ARQ scheme. The total number of bytes sent by all nodes in the multi-hop communication is reduced by more than 60% compared to the frame ARQ scheme.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.50-53
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• 2017

Not only growth of importance to understanding economic trends, but also the prediction to overcome the uncertainty is coming up for long-term maritime recession. This paper discussed about the prediction of BDI with artificial neural networks (ANN). ANN is one of emerging applications that can be the finest solution to the knotty problems that may not easy to achieve by humankind. Proposed a prediction by implementing neural networks that have recurrent architecture which are a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). And for the reason of comparison, trained Multi Layer Perceptron (MLP) from 2009.04.01 to 2017.07.31. Also made a comparison with conventional statistics, prediction tools; ARIMA. As a result, recurrent net, especially RNN outperformed and also could discover the applicability of LSTM to specific time-series (BDI).

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.147-153
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• 2018

Beacon enables one to measure distance indoors based on low-power Bluetooth low energy (BLE) technology, while GPS (Global Positioning System) only can be used outdoors. In measuring indoor distance using Beacon, RSSI (Received Signal Strength Indication) is considered as the one of the key factors, however, it is influenced by various environmental factors so that it causes the huge gap between the estimated distance and the real. In order to handle this issue, we propose the adaptive ANN (Artificial Neural Network) based approach to measuring the exact distance using Beacon. First, we has carried out the preprocessing of the RSSI signals by applying the extended Kalman filter and the signal stabilization filter into decreasing the noise. Then, we suggest the multi-layered ANNs, each of which layer is learned by specific training data sets. The results showed an average error of 0.67m, a precision of 0.78.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Journal of Advanced Navigation Technology
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• v.24 no.1
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• pp.47-52
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• 2020

Direction of arrival (DoA) estimation is a scheme of estimating the directions of targets by analyzing signals generated or reflected from the targets and is used in various fields. Artificial neural networks (ANN) is a field of machine learning that mimics the neural network of living organisms. They show good performance in pattern recognition. Although researches has been using ANNs to estimate the DoAs, there are limitationsin dealing with variations of the signal-to-noise ratio (SNR) of the target signals. In this paper, we propose a three-stage ANN algorithm for DoA estimation. The proposed algorithm can minimize the performance reduction by applying the model trained in a single SNR environment to various environments through a 'noise reduction process'. Furthermore, the scheme reduces the difficulty in learning and maintains efficiency in estimation, by employing a process of DoA shift. We compare the performance of the proposed algorithm with Cramer-Rao bound (CRB) and the performances of existing subspace-based algorithms and show that the proposed scheme exhibits better performance than other schemes in some severe environments such as low SNR environments or situations in which targets are located very close to each other.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.12
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• pp.1380-1390
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• 1992

This paper proposes an on-line recognition system of Korean strokes using multi-layer neural network with tracing the stroke pattern. The system segments the stroke pattern into subpatterns, detects prominent stroke features in the subpatterns and integrates all the activation values of features in the related subpatterns. The activation values of the integrated stroke-specific features represent statistic characteristics of features and contributes for classifying the stroke pattern. Since the informations in Korean strokes are concentrated in the first and last parts of the strokes, the system extracts stroke-specific features in these parts attentatively and infers corner features using the sequential information of the extracted stroke-specific features in the first and last part of strokes the system is relatively simple in structure and rapid in on-line recognition of hand-written Korean strokes.