• The Journal of the Korea Contents Association
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• v.11 no.6
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• pp.51-58
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• 2011

One of the objectives of research on routing methods in wireless sensor networks is maximizing the energy life of sensor nodes that have limited energy. In this study, we tried to even energy use in a wireless sensor network by giving a weight to the transition probability of ACS(Ant Colony System), which is commonly used to find the optimal path, based on the amount of energy in a sensor and the distance of the sensor from the sink. The proposed method showed improvement by 46.80% on the average in energy utility in comparison with representative routing method GPSR (Greedy Perimeter Stateless Routing), and its residual energy after operation for a specific length of time was 6.7% more on the average than that in ACS.

• Wind and Structures
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• v.20 no.6
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• pp.763-791
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• 2015

"Wind and Ports" is a European project that has been carried out since 2009 to handle wind forecast in port areas through an integrated system made up of an extensive in-situ wind monitoring network, the numerical simulation of wind fields, the statistical analysis of wind climate, and algorithms for medium-term (1-3 days) and short term (0.5-2 hours) wind forecasting. The in-situ wind monitoring network, currently made up of 22 ultrasonic anemometers, provides a unique opportunity for detecting high resolution thunderstorm records and studying their dominant characteristics relevant to wind engineering with special concern for wind actions on structures. In such a framework, the wind velocity of thunderstorms is firstly decomposed into the sum of a slowly-varying mean part plus a residual fluctuation dealt with as a non-stationary random process. The fluctuation, in turn, is expressed as the product of its slowly-varying standard deviation by a reduced turbulence component dealt with as a rapidly-varying stationary Gaussian random process with zero mean and unit standard deviation. The extraction of the mean part of the wind velocity is carried out through a moving average filter, and the effect of the moving average period on the statistical properties of the decomposed signals is evaluated. Among other aspects, special attention is given to the thunderstorm duration, the turbulence intensity, the power spectral density and the integral length scale. Some noteworthy wind velocity ratios that play a crucial role in the thunderstorm loading and response of structures are also analyzed.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.43-53
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• 2006

As wireless network technology has advanced, demands for multimedia contents through mobile environment have tendered to upward. Since network situation is changing every moment and types of user terminals are diverse, it is difficult for a content provider to consider network situation and type of user terminal to provide multimedia contents. As one solution, transcoding techniques have been proposed, but those have much complexity. In this paper, in order to reduce computational complexity, we propose a fast mode decision using input modes, motion vectors, and residual energies which are obtained from input bitstream for 2:1 down-scaling spatial transcoding application. The proposed method reduces processing time in mode decision by restricting possible mode types based on input information. Experimental results show that the proposed method achieves about 2.66 times improvement in encoding time compared to the normal encoding process while the PSNR is degraded by about 0.04dB, and bit-rate is increased by 1.6%.

• The Journal of the Korea Contents Association
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• v.10 no.2
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• pp.14-24
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• 2010

Because WSNs operate with limited resources of sensor nodes, its life is extended by cluster-based routing methods. In this study, we use data on direction, distance, density and residual energy in order to maximize the energy efficiency of cluster-based routing methods. Through this study, we expect to minimize the frequency of isolated nodes when selecting a new cluster head autonomously using information on the direction of the upper cluster head, and to reduce energy consumption by switching sensor nodes, which are included in both of the new cluster and the previous cluster and thus do not need to update information, into the sleep mode and updating information only for newly included sensor nodes at the setup phase using distance data. Furthermore, we enhance overall network efficiency by implementing secure and energy-efficient communication through key management robust against internal and external attacks in cluster-based routing techniques. This study suggests the modified cluster head selection scheme which uses the conserved energy in the steady-state phase by reducing unnecessary communications of unchanged nodes between selected cluster head and previous cluster head in the setup phase, and thus prolongs the network lifetime and provides secure and equal opportunity for being cluster head.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4411-4431
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• 2014

Mobile ad hoc networks (MANETs) have recently gained increased interest due to the widespread use of smart mobile devices. Group communication applications, serving for better cooperation between subsets of business members, become more significant in the context of MANETs. Multicast routing mechanisms are very useful communication techniques for such group-oriented applications. This paper deals with multicast routing problems in terms of stability and scalability, using the concept of stable core. We propose LMRSC (Lightweight Multicast Routing Based on Stable Core), a lightweight multicast routing technique for MANETs, in order to avoid periodic flooding of the source messages throughout the network, and to increase the duration of multicast routes. LMRSC establishes and maintains mesh architecture for each multicast group member by dividing the network into several zones, where each zone elects the most stable node as its core. Node residual energy and node velocity are used to calculate the node stability factor. The proposed algorithm is simulated by using NS-2 simulation, and is compared with other multicast routing mechanisms: ODMRP and PUMA. Packet delivery ratio, multicast route lifetime, and control packet overhead are used as performance metrics. These metrics are measured by gradual increase of the node mobility, the number of sources, the group size and the number of groups. The simulation performance results indicate that the proposed algorithm outperforms other mechanisms in terms of routes stability and network density.

• The Journal of the Acoustical Society of Korea
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• v.38 no.6
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• pp.670-677
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• 2019

As the presence of background noise in acoustic signal degrades the performance of speech or acoustic event recognition, it is still challenging to extract noise-robust acoustic features from noisy signal. In this paper, we propose a combined structure of Wasserstein Generative Adversarial Network (WGAN) and MultiTask AutoEncoder (MTAE) as deep learning architecture that integrates the strength of MTAE and WGAN respectively such that it estimates not only noise but also speech features from noisy acoustic source. The proposed MTAE-WGAN structure is used to estimate speech signal and the residual noise by employing a gradient penalty and a weight initialization method for Leaky Rectified Linear Unit (LReLU) and Parametric ReLU (PReLU). The proposed MTAE-WGAN structure with the adopted gradient penalty loss function enhances the speech features and subsequently achieve substantial Phoneme Error Rate (PER) improvements over the stand-alone Deep Denoising Autoencoder (DDAE), MTAE, Redundant Convolutional Encoder-Decoder (R-CED) and Recurrent MTAE (RMTAE) models for robust speech recognition.

• Journal of the Korean Geotechnical Society
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• v.36 no.3
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• pp.5-14
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• 2020

The railway concrete track has been increasingly adopted for high-speed train such as KTX due to its high running stability, improved ride quality for the passengers, and low maintenance cost. However, excessive settlement of the railway concrete track has been monitored at embankment sections of the ◯◯ High-speed Line, resulting in the concerns on the safety of railway operation. In order to establish an effective maintenance plan for the concrete track railway exceeding the allowable residual settlement, it is essential to reasonably predict their long-term settlement behavior during the public period. In this study, we developed a model for predicting the long-term settlement behavior of concrete track using recurrent neural network (RNN) and examined the applicability of the developed model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.4
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• pp.515-521
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• 2020

Recently, data traffic has exploded due to development of various industries, which causes problems about losing of efficiency and overloaded existing networks. To solve these problems, network slicing, which uses a virtualization technology and provides a network optimized for various services, has received a lot of attention. In this paper, we propose a resource allocation method using credit value. In the method using the clustering technology, an operation for selecting a cluster is performed whenever an allocation request for various services occurs. On the other hand, in the proposed method, the credit value is set by using the residual capacity and balancing so that the slice request can be processed without performing the operation required for cluster selection. To prove proposed method, we perform processing time and balancing simulation. As a result, the processing time and the error factor of the proposed method are reduced by about 13.72% and about 7.96% compared with the clustering method.

• Geomechanics and Engineering
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• v.28 no.6
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• pp.599-611
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• 2022

Tendon reinforced cemented soil is applied extensively in foundation stabilisation and improvement, especially in areas with soft clay. To solve the deterioration problem led by steel corrosion, the glass fiber-reinforced polymer (GFRP) tendon is introduced to substitute the traditional steel tendon. The interface bond strength between the cemented soil matrix and GFRP tendon demonstrates the outstanding mechanical property of this composite. However, the lack of research between the influence factors and bond strength hinders the application. To evaluate these factors, back propagation neural network (BPNN) is applied to predict the relationship between them and bond strength. Since adjusting BPNN parameters is time-consuming and laborious, the particle swarm optimisation (PSO) algorithm is proposed. This study evaluated the influence of water content, cement content, curing time, and slip distance on the bond performance of GFRP tendon-reinforced cemented soils (GTRCS). The results showed that the ultimate and residual bond strengths were both in positive proportion to cement content and negative to water content. The sample cured for 28 days with 30% water content and 50% cement content had the largest ultimate strength (3879.40 kPa). The PSO-BPNN model was tuned with 3 neurons in the input layer, 10 in the hidden layer, and 1 in the output layer. It showed outstanding performance on a large database comprising 405 testing results. Its higher correlation coefficient (0.908) and lower root-mean-square error (239.11 kPa) were obtained compared to multiple linear regression (MLR) and logistic regression (LR). In addition, a sensitivity analysis was applied to acquire the ranking of the input variables. The results illustrated that the cement content performed the strongest influence on bond strength, followed by the water content and slip displacement.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1477-1485
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• 2021

Recently, super-resolution has been intensively studied only on upscaling models with integer magnification. However, the need to expand arbitrary magnification is emerging in representative application fields of actual super-resolution, such as object recognition and display image quality improvement. In this paper, we propose a model that can support arbitrary magnification by using the weights of the existing integer magnification model. This model converts super-resolution results into the DCT spectral domain to expand the space for arbitrary magnification. To reduce the loss of high-frequency information in the image caused by the expansion by the DCT spectral domain, we propose a high-frequency attention network for arbitrary magnification so that this model can properly restore high-frequency spectral information. To recover high-frequency information properly, the proposed network utilizes channel attention layers. This layer can learn correlations between RGB channels, and it can deepen the model through residual structures.