• Applied Chemistry for Engineering
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• v.30 no.2
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• pp.133-140
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• 2019

Two-dimensional (2D) ultrathin molybdenum dichalcogenides $MoS_2$ has gained a great deal of attention in energy conversion and storage applications because of its unique morphology and property. The 2D $MoS_2$ nanosheets provide a high specific surface area, 2D charge channel, sub-nanometer thickness, and high conductivity, which lead to high electrochemical performances for energy storage devices. In this paper, an overview of properties and synthetic methods of $MoS_2$ nanosheets for applications of supercapacitors and rechargeable batteries is introduced. Different phases triangle prismatic 2H and metallic octahedral 1T structured $MoS_2$ were characterized using various analytical techniques. Preparation methods were focused on top-down and bottom-up approaches, including mechanical exfoliation, chemical intercalation and exfoliation, liquid phase exfoliation by the direct sonication, electrochemical intercalation exfoliation, microwave-assisted exfoliation, mechanical ball-milling, and hydrothermal synthesis. In addition, recent applications of supercapacitors and rechargeable batteries using $MoS_2$ electrode materials are discussed.

• Journal of Korea Multimedia Society
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• v.25 no.1
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• pp.52-60
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• 2022

Few-shot learning is a sub-area of machine learning problems, which aims to classify target images that only contain a few labeled samples for training. As a representative few-shot learning method, the Prototypical network has been received much attention due to its simplicity and promising results. However, the Prototypical network uses the sample mean of samples from the same class as the prototypes of that class, which easily results in learning uncharacteristic features in the low-data scenery. In this study, we propose to use local descriptors (i.e., patches along the channel within feature maps) from the same class to explicitly obtain more representative prototypes for Prototypical Network so that significant intra-class feature information can be maintained and thus improving the classification performance on few-shot learning tasks. Experimental results on various benchmark datasets including mini-ImageNet, CUB-200-2011, and tiered-ImageNet show that the proposed method can learn more discriminative intra-class features by the local descriptors and obtain more generic prototype representations under the few-shot setting.

• Journal of Korea Multimedia Society
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• v.25 no.5
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• pp.676-684
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• 2022

As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.60-79
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• 2022

The U-Net architecture-based segmentation models attained remarkable performance in numerous medical image segmentation missions like skin lesion segmentation. Nevertheless, the resolution gradually decreases and the loss of spatial information increases with deeper network. The fusion of adjacent layers is not enough to make up for the lost spatial information, thus resulting in errors of segmentation boundary so as to decline the accuracy of segmentation. To tackle the issue, we propose a new deep learning-based segmentation model. In the decoding stage, the feature channels of each decoding unit are concatenated with all the feature channels of the upper coding unit. Which is done in order to ensure the segmentation effect by integrating spatial and semantic information, and promotes the robustness and generalization of our model by combining the atrous spatial pyramid pooling (ASPP) module and channel attention module (CAM). Extensive experiments on ISIC2016 and ISIC2017 common datasets proved that our model implements well and outperforms compared segmentation models for skin lesion segmentation.

• Asia Marketing Journal
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• v.21 no.4
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• pp.59-76
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• 2020

Though crowdfunding has emerged as a cost-effective means to market innovative products and a channel for financial resource acquisition, our understanding about what makes a successful crowdfunding is still limited. This study is motivated by the presence of the inconsistency that both entrepreneurs and investors pay more attention to capabilities required for developing prototype products rather than capabilities needed to deliver the promised products in crowdfunding. By drawing insights from studies about liability of newness and legitimacy in institutional theory, this study argues that startups can overcome the liability of newness, earn investor trust, and successfully complete crowdfunding campaigns by effectively presenting visible and invisible capabilities. This study presents a set of testable propositions predicting the likelihood of crowdfunding success and explains the theoretical and practical value of the proposed conceptualization of startup capabilities.

• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.137-140
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• 2022

The article deals with increasing the effectiveness of educational content on the YouTube channel with the help of built-in analytics tools. Particular attention is paid to the creation of quality video, taking into account the reaction of end users of video materials. Ways to increase the video audience are considered. Techniques for improving video content apply to individual videos, as well as playlists and channels in general. In this case YouTube is both an independent educational platform and an auxiliary platform in a set of various educational solutions.

• Electronics and Telecommunications Trends
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• v.39 no.1
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• pp.25-35
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• 2024

Wireless communication, including mobile networks and local area networks, has become an essential service in the society. Wireless communication is evolving to include sensing services, as demonstrated by the increasing attention in organizations and standards such as 3GPP and IEEE 802.11. This survey presents technology trends in the integration of sensing and communication. The standardization status along with use cases provided by standards are explained. In addition, core and supporting technologies such as channel modeling, waveform design, and artificial intelligence are analyzed.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1827-1840
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• 2001

In digital data transmission/storage systems, the compensation for channel distortion is conducted normally using a training sequence that is known a priori to both the sender and receiver. The use of the training sequences results in inefficient utilization of channel bandwidth. Sometimes, it is also impossible to send training sequences such as in the burst-mode communication. As such, a great deal of attention has been given to the approach requiring no training sequences, which has been called the blind equalization technique. On the other hand, to utilize the limited bandwidth effectively, the concept of partial response (PR) signaling has widely been adopted in both the high-speed transmission and high-density recording/playback systems such as digital microwave, digital subscriber loops, hard disk drives, digital VCRs and digital versatile recordable disks and so on. This paper is concerned with blind adaptive equalization of partial response channels whose transfer function zeros are located on the unit circle, thereby causing some problems in performance. Specifically we study how the problems of blind channel equalization associated with the PR channels can be improved. In doing so, we first discuss the existing methods and then propose new structures for blind PR channel equalization. Our structures have been extensively tested by computer simulation and found out to be encouraging in performance. The results seem very promising as well in terms of the implementation complexity compared to the previous approach reported in literature.

• Journal of Advanced Navigation Technology
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• v.27 no.6
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• pp.835-840
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• 2023

RFID(Radio Frequency Identification) systems are attracting attention as a key component of Internet of Things technology due to the cost and energy efficiency of application services. In order to use RFID technology in the IoT application service field, it is necessary to be able to store and manage various information for a long period of time as well as simple recognition between the reader and tag of the RFID system. And in order to read and write information to tags, a performance improvement technology that is strong and reliable in poor wireless channels is needed. In particular, in the UHF(Ultra High Frequency) RFID system, since multiple tags communicate passively in a crowded environment, it is essential to improve the recognition rate and transmission speed of individual tags. In this paper, Middleton's Class A impulsive noise model was selected to analyze the performance of the RFID system in an impulsive noise environment, and FM0 encoding and Miller encoding were applied to the tag to analyze the error rate performance of the RFID system. As a result of analyzing the performance of the RFID system in Middleton's Class A impulsive noise channel, it was found that the larger the Gaussian noise to impulsive noise power ratio and the impulsive noise index, the more similar the characteristics to the Gaussian noise channel.