• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1321-1329
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• 2019

With the development of social information networks, the marketing methods are also changing in various ways. Unlike successful marketing methods based on existing celebrities and financial support, Influencer-based marketing is a big trend and very famous. In this paper, we first extract influencer features from more than 54 YouTube channels using the multi-dimensional qualitative analysis based on the meta information and comment data analysis of YouTube, model representative themes to maximize a personalized video satisfaction. Plus, the purpose of this study is to provide supplementary means for the successful promotion and marketing by creating and distributing videos of new items by referring to the existing Influencer features. For that we assume all comments of various videos for each channel as each document, TF-IDF (Term Frequency and Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) algorithms are applied to maximize performance of the proposed scheme. Based on the performance evaluation, we proved the proposed scheme is better than other schemes.

• Journal of the Economic Geographical Society of Korea
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• v.25 no.1
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• pp.61-86
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• 2022

This paper analyzes the technology transfer mechanism of companies clustered in a specific economic space from the perspective of absorptive capacity, taking the Daegu automotive parts industry as an example. According to the analysis results, The absorptive capacity of a company was found to be positively related to the supplier, channel, method, and institutional interaction of technology transfer. Low absorptive capacity limits technology transfer by reducing companies' technology search capabilities, while high absorptive capacity gives companies access to technology search and various technology transfer opportunities through formal and informal channels. These results suggest that, in the short term, it is necessary to supplement the search capabilities of companies through preemptive visiting services of local intermediaries for the vitalization of regional technology transfer, while in the long term, a policy approach focusing on enhancing the absorptive capacity of local companies is required. For effective implementation of these policies, a multi-layered governance approach corresponding to each level of companies' absorptive capacities is required in establishing an interface structure that promotes linkage between institutional actors.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.107-115
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• 2023

This study analyzes the content of YouTube Vlog videos created by patients of Coronavirus disease 2019 ("COVID-19") in South Korea and viewer comments on those videos. As this new infectious disease started to sweep the world in late 2019 and early 2020, the public started facing fear and uncertainty stemming from the lack of sufficient and accurate information about the virus. At the same time, as COVID-19 patients in South Korea were treated in isolation to prevent the spread of the virus, the patients themselves were experiencing anxiety and exclusion from the society. During this period, there was an increase in YouTube Vlog videos created by the patients in which they shared their experiences going through the treatment and recovery processes. To understand how these YouTube Vlog videos were being used by the patients to connect with the society and seek support in a state of isolation and anxiety, this study conducted a qualitative multi-case analysis of three sample YouTube Vlog video channels to analyze their content, as well as a lexicon-based sentiment analysis of viewer comments to understand the experiences and reactions of viewers. The patients' YouTube Vlog videos showed that they shared similar stages of progress, despite each emphasizing a different main theme. Overall, the tone of the viewer comments became increasingly positive over time, although with some variance among different patient cases and stages. The results confirmed that Vlogs of patients played a significant role in reducing the uncertainty around COVID-19 and strengthening social support for the patients. The findings of this study can improve an understanding of the psychological and behavioral aspects of patient experience in isolated treatment and the impact of shared communication among members of society in times of crisis.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.19-28
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• 2017

Various methods for reducing the load on the server have been implemented in performing face recognition remotely by the spread of IP security cameras. In this paper, IP surveillance cameras at remote sites are input through a DSP board equipped with face detection function, and then face detection is performed. Then, the facial region image is transmitted to the server, and the face recognition processing is performed through face recognition distributed processing. As a result, the overall server system load and significantly reduce processing and real-time face recognition has the advantage that you can perform while linked up to 256 cameras. The technology that can accomplish this is to perform 64-channel face recognition per server using distributed processing server technology and to process face search results through 250 camera channels when operating four distributed processing servers there was.

• The Journal of the Acoustical Society of Korea
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• v.42 no.4
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• pp.357-363
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• 2023

In underwater signal processing, separating individual signals from mixed signals has long been a challenge due to low signal quality. The common method using Short-time Fourier transform for spectrogram analysis has faced criticism for its complex parameter optimization and loss of phase data. We propose a Triple-path Recurrent Neural Network, based on the Dual-path Recurrent Neural Network's success in long time series signal processing, to handle three-dimensional tensors from multi-channel sensor input signals. By dividing input signals into short chunks and creating a 3D tensor, the method accounts for relationships within and between chunks and channels, enabling local and global feature learning. The proposed technique demonstrates improved Root Mean Square Error and Scale Invariant Signal to Noise Ratio compared to the existing method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• The Journal of the Acoustical Society of Korea
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• v.27 no.4
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• pp.207-214
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• 2008

To create 3D sound, we usually use two methods which are two channels or multichannel sound systems. Because of cost and space problems, we prefer two channel sound system to multi-channel. Using a headphone or two speakers, the most typical method to create 3D sound effects is a technology of head related transfer function (HRTF) which contains the information that sound arrives from a sound source to the ears of the listener. But it causes a problem to localize a sound source around a certain places which is called cone-of-confusion. In this paper, we proposed the new algorithm to reduce the confusion of sound image localization. HRTF grouping and psychoacoustics theory are used to boost the spectral cue with spectrum difference among each directions. Informal listening tests show that the proposed method improves the front-back sound localization characteristics much better than conventional methods.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.3
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• pp.277-284
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• 2012

In this study, a heat-transfer performance analysis is carried out for a multi-channel volumetric air receiver for a solar power tower. On the basis of a series of reviews regarding the relevant literature, a calculation process is proposed for the prediction of the wall- and air- temperature distributions of a single channel at given geometric and input conditions. Furthermore, a unique mathematical model of the receiver effectiveness is presented through analysis of the temperature profile. The receiver is made of silicon carbide. A total of 225 square straight channels per module are molded to induce the air flow, and each channel has the dimensions of $2mm(W){\times}2mm(H){\times}0.2mm(t){\times}320mm(L)$. The heat-transfer rate, temperature distribution and effectiveness are presented according to the variation of the channel and module number under uniform irradiation and mass flow rate. The available air outlet temperature applied to the solar power tower should be over $700^{\circ}C$. This numerical model was actually used in the design of a 200 kW-level commercial solar air receiver, and the required number of modules satisfying the thermal performance could be obtained for the specified geometric and input conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.8A
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• pp.777-784
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• 2010

Recently, the necessity of underwater communication and demand for transmitting and receiving various data such as voice or high resolution image data are increasing as well. The performance of underwater acoustic communication system is influenced by characteristics of the underwater communication channels. Especially, ISI(inter symbol interference) occurs because of delay spread according to multi-path and communication performance is degraded. In this paper, we study the OFDM technique to overcome the delay spread in underwater channel and by using CP, we compensate for delay spread. But PAPR which OFDM system has problem is very high. Therefore, we use DFT-spread OFDM method to avoid nonlinear distortion by high PAPR and to improve efficiency of amplifier. DFT-spread OFDM technique obtains high PAPR reduction effect because of each parallel data loads to all subcarrier by DFT spread processing before IFFT. In this paper, we show performance about delay spread through OFDM system and verify method that DFT spread OFDM is more suitable than OFDM for underwater communication. And we analyze performance according to two subcarrier mapping methods(Interleaved, Localized). Through the simulation results, performance of DFT spread OFDM is better about 5~6dB at $10^{-4}$ than OFDM. When compared to BER according to subcarrier mapping, Interleaved method is better about 3.5dB at $10^{-4}$ than Localized method.