• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.3
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• pp.430-435
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• 2022

In this paper, we consider the non-orthogonal multiple access (NOMA) technique in the heterogeneous network (HetNET) consisting of a single macro base station (BS) and multiple small BSs, where the perfect successive interference cancellation is assumed for the NOMA signals. In this paper, we propose a deep learning-based user association and power allocation scheme to maximize the data rate in the NOMA-based HetNET. In particular, the proposed scheme includes the deep neural network (DNN)-based user association process for load balancing and the DNN-based power allocation process for data-rate maximization. Through the simulation assuming path loss and Rayleigh fading channels between BSs and users, the performance of the proposed scheme is evaluated, and it is compared with the conventional maximum signal-to-interference-plus-noise ratio (Max-SINR) scheme. Through the performance comparison, we show that the proposed scheme provides better sum rate performance than the conventional Max-SINR scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1566-1573
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• 2021

Rendezvous is a process that assists nodes in a Cognitive Radio Networks (CRNs) to discover each other. In CRNs where a common control channel is unknown and a number of channels are given, it is important how two nodes find each other in a known search region. In this paper, I have proposed and analyzed a channel hopping sequence using Sidel'nikov sequence by which each node visits an available number of channels. I analyze the expected time to-rendezvous (TTR) mathematically. I also verify the Rendezvous performance of proposed sequence in the view of TTR under 2 user environment compared with JS algorithm and GOS algorithm. The Rendezvous performance of proposed sequence is much better than GOS algorithm and similar with JS algorithm. But when M is much smaller than p, the performance of proposed sequence is better than JS algorithm.

• Molecules and Cells
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• v.44 no.11
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• pp.784-794
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• 2021

Leiomyosarcoma (LMS) is a mesenchymal malignancy with a complex karyotype. Despite accumulated evidence, the factors contributing to the development of LMS are unclear. Here, we investigated the role of tight-junction protein 1 (TJP1), a membrane-associated intercellular barrier protein during the development of LMS and the tumor microenvironment. We orthotopically transplanted SK-LMS-1 cells and their derivatives in terms of TJP1 expression by intramuscular injection, such as SK-LMS-1 Sh-Control cells and SK-LMS-1 Sh-TJP1. We observed robust tumor growth in mice transplanted with LMS cell lines expressing TJP1 while no tumor mass was found in mice transplanted with SK-LMS-1 Sh-TJP1 cells with silenced TJP1 expression. Tissues from mice were stained and further analyzed to clarify the effects of TJP1 expression on tumor development and the tumor microenvironment. To identify the TJP1-dependent factors important in the development of LMS, genes with altered expression were selected in SK-LMS-1 cells such as cyclinD1, CSF1 and so on. The top 10% of highly expressed genes in LMS tissues were obtained from public databases. Further analysis revealed two clusters related to cell proliferation and the tumor microenvironment. Furthermore, integrated analyses of the gene expression networks revealed correlations among TJP1, CSF1 and CTLA4 at the mRNA level, suggesting a possible role for TJP1 in the immune environment. Taken together, these results imply that TJP1 contributes to the development of sarcoma by proliferation through modulating cell-cell aggregation and communication through cytokines in the tumor microenvironment and might be a beneficial therapeutic target.

• Journal of Internet Computing and Services
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• v.23 no.3
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• pp.1-12
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• 2022

As the performance of Unmanned Aerial Vehicles (UAVs) are improving and the prices are lowering, it is expected that the use of UAVs will continuously grow in the future. It is important to always maintain control signal and video communication to operate remote UAVs stably, especially in military UAV operations, as unexpected jamming attacks can result in fatal UAV crashes. In this paper, to improve the network reliability and low latency when supporting UAV operations, the anti-jamming performance of Second generation Anti-jam Tactical UHF Radio for NATO (SATURN) networks is analyzed and enhanced by applying Forward Error Correction (FEC) and Minimum Shift Keying (MSK) modulation as well as Hamming code based multiple transmission techniques.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1453-1461
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• 2022

The accuracy of peripheral object recognition algorithms using 3D data sensors such as LiDAR in autonomous vehicles has been increasing through many studies, but this requires high performance hardware and complex structures. This object recognition algorithm acts as a large load on the main processor of an autonomous vehicle that requires performing and managing many processors while driving. To reduce this load and simultaneously exploit the advantages of 3D sensor data, we propose 2D data-based recognition using the ROI generated by extracting physical properties from 3D sensor data. In the environment where the brightness value was reduced by 50% in the basic image, it showed 5.3% higher accuracy and 28.57% lower performance time than the existing 2D-based model. Instead of having a 2.46 percent lower accuracy than the 3D-based model in the base image, it has a 6.25 percent reduction in performance time.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.527-541
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• 2022

A company is a social group of many individuals that work together to obtain better results, and it is an organization that pursues common goals such as profit. As a result, forming networks among members, as well as individual communication abilities, is critical. The purpose of this research was to determine what factors influence the creation of employee advice relationships. Using the ERGM(Exponential Random Graph Model) approach, we looked at the network data of 44 individuals from consulting firms with offices in the United States and Europe. The significance of structural network factors like connectivity was first discovered. Second, the gender factor had the most significant main influence on the likelihood of adopting each other's advice. Third, geographical homogeneity resulted in higher link probabilities than major impacts of gender. This research looked at ways to make a company's network more efficient and active.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.8
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• pp.1121-1129
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• 2022

Object recognition is carried out using RGB images in various object recognition studies. However, RGB images in dark illumination environments or environments where target objects are occluded other objects cause poor object recognition performance. On the other hand, IR images provide strong object recognition performance in these environments because it detects infrared waves rather than visible illumination. In this paper, we propose an RGB-IR fusion model, high-frequency interchange network (HINet), which improves object recognition performance by combining only the strengths of RGB-IR image pairs. HINet connected two object detection models using a mutual high-frequency transfer (MHT) to interchange advantages between RGB-IR images. MHT converts each pair of RGB-IR images into a discrete cosine transform (DCT) spectrum domain to extract high-frequency information. The extracted high-frequency information is transmitted to each other's networks and utilized to improve object recognition performance. Experimental results show the superiority of the proposed network and present performance improvement of the multispectral object recognition task.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.667-673
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• 2021

Unauthorized generations and sales of non-funable tokens (NFTs) without the consent of the creator is one of the biggest problems that arise in NFT Marketplaces. This study proposes a method to practically reduce the problem of NFT sales without the consent of the creator by means of authentication with social media accounts. Through the proposed method, creators who are already using social media as a means of communication and marketing for creative activities can authenticate with their own accounts. Creators who have difficulty authenticating with their own accounts will be provided with alternatives to authenticate using human networks. In addition, the proposed method of protecting creator rights was designed using a flowchart to enable development using only the public API (Application Programming Interface) provided by social media. The proposed method can protect creators' rights and reduce damage caused by NFT fraud by inducing buyers to trade NFTs of authorized sellers through social media.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.2
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• pp.306-312
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• 2022

During the unprecedented COVID-19 outbreak, the public's information needs created an environment where they overwhelmingly consume information on the chronic disease. Given that news media affect the public's emotional well-being, the pandemic situation highlights the importance of paying particular attention to how news stories frame their coverage. In this study, COVID-19 news speech emotion from mainstream broadcasters in South Korea and the United States (US) were analyzed using convolutional neural networks. Results showed that neutrality was detected across broadcasters. However, emotions such as sadness and anger were also detected. This was evident in Korean broadcasters, whereas those emotions were not detected in the US broadcasters. This is the first quantitative vocal emotion analysis of COVID-19 news speech. Overall, our findings provide new insight into news emotion analysis and have broad implications for better understanding of the COVID-19 pandemic.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1630-1636
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• 2022

The results of current method of traffic sign detection gets hindered by environmental conditions and the traffic sign's condition as well. Therefore, in this paper, we propose a method of improving detection performance of damaged traffic signs by utilizing Hopfield Network and Fuzzy Max-Min Neural Network. In this proposed method, the characteristics of damaged traffic signs are analyzed and those characteristics are configured as the training pattern to be used by Fuzzy Max-Min Neural Network to initially classify the characteristics of the traffic signs. The images with initial characteristics that has been classified are restored by using Hopfield Network. The images restored with Hopfield Network are classified by the Fuzzy Max-Min Neural Network onces again to finally classify and detect the damaged traffic signs. 8 traffic signs with varying degrees of damage are used to evaluate the performance of the proposed method which resulted with an average of 38.76% improvement on classification performance than the Fuzzy Max-Min Neural Network.