• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.450-451
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• 2013

Considering the recent few years, the usage of mobile content has increased rapidly. This brings out the need for the new internet paradigm. Content-Centric Networking (CCN) caters this need as the future internet paradigm. However, so far, the issue of mobility in the network using CCN has not been considered very efficiently. In this paper, we propose clustering in the network. We apply clustered approach to CCN for catering the mobility of client node in the network. Through this approach we achieve better convergence time and control overhead in contrast to the basic CCN.

• Journal of Korea Multimedia Society
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• v.24 no.8
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• pp.1068-1075
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• 2021

Wireless sensor network (WSN) can be usefully used in battlefields requiring rapid installation and operation by enabling surveillance and reconnaissance using small sensors in areas where any existing network infrastructure is not formed. As WSN uses battery, energy efficiency acts as a very important issue in network survivability. Layer-based routing protocols have been studied a lot in the aspect of energy efficiency. Many research selected LEACH and PEGASIS protocols as their comparison targets. This study examines the two protocols and LECEEP, a protocol designed by combining their advantages, and proposes a new protocol, A-LECEEP, which is more energy efficient than the others. The proposed protocol can increase energy efficiency compared to the existing ones by eliminating unnecessary transmissions with multiple chains configuration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.861-880
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• 2023

Semantic segmentation of road scene is the key technology of autonomous driving, and the improvement of convolutional neural network architecture promotes the improvement of model segmentation performance. The existing convolutional neural network has the simplification of learning knowledge and the complexity of the model. To address this issue, we proposed a road scene semantic segmentation algorithm based on multi-task collaborative learning. Firstly, a depthwise separable convolution atrous spatial pyramid pooling is proposed to reduce model complexity. Secondly, a collaborative learning framework is proposed involved with saliency detection, and the joint loss function is defined using homoscedastic uncertainty to meet the new learning model. Experiments are conducted on the road and nature scenes datasets. The proposed method achieves 70.94% and 64.90% mIoU on Cityscapes and PASCAL VOC 2012 datasets, respectively. Qualitatively, Compared to methods with excellent performance, the method proposed in this paper has significant advantages in the segmentation of fine targets and boundaries.

• ETRI Journal
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• v.45 no.4
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• pp.594-602
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• 2023

Internet of things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Systemic lupus erythematosus (SLE) is an autoimmune illness that occurs when the body's immune system attacks its own connective tissues and organs. Because of the complicated interconnections between illness trigger exposure levels across time, humans have trouble predicting SLE symptom severity levels. An effective automated machine learning model that intakes IoT data was created to forecast SLE symptoms to solve this issue. IoT has several advantages in the healthcare industry, including interoperability, information exchange, machine-to-machine networking, and data transmission. An SLE symptom-predicting machine learning model was designed by integrating the hybrid marine predator algorithm and atom search optimization with an artificial neural network. The network is trained by the Gene Expression Omnibus dataset as input, and the patients' data are used as input to predict symptoms. The experimental results demonstrate that the proposed model's accuracy is higher than state-of-the-art prediction models at approximately 99.70%.

• Electronics and Telecommunications Trends
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• v.38 no.6
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• pp.95-106
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• 2023

As artificial intelligence has become commonplace in various fields, the transparency of AI in its development and implementation has become an important issue. In safety-critical areas, the eXplainable and/or understandable of artificial intelligence is being actively studied. On the other hand, machine learning have been applied to the intelligence of self-organizing network (SON), but transparency in this application has been neglected, despite the critical decision-makings in the operation of mobile communication systems. We describes concepts of eXplainable machine learning (ML), along with research trends, major issues, and research directions. After summarizing the ML research on SON, research directions are analyzed for explainable ML required in intelligent SON of beyond 5G and 6G communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3145-3162
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• 2023

As an important technology of the internetwork, wireless sensor network technique plays an important role in indoor localization. Non-line-of-sight (NLOS) problem has a large effect on indoor location accuracy. A location algorithm based on improved particle filter and directional probabilistic data association (IPF-DPDA) for WSN is proposed to solve NLOS issue in this paper. Firstly, the improved particle filter is proposed to reduce error of measuring distance. Then the hypothesis test is used to detect whether measurements are in LOS situations or NLOS situations for N different groups. When there are measurements in the validation gate, the corresponding association probabilities are applied to weight retained position estimate to gain final location estimation. We have improved the traditional data association and added directional information on the original basis. If the validation gate has no measured value, we make use of the Kalman prediction value to renew. Finally, simulation and experimental results show that compared with existing methods, the IPF-DPDA performance better.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.1-6
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• 2024

Energy efficiency in wireless sensor networks (WSNs) is a critical issue because batteries are used for operation and communication. In terms of scalability, energy efficiency, data integration, and resilience, WSN-cluster-based routing algorithms often outperform routing algorithms without clustering. Low-energy adaptive clustering hierarchy (LEACH) is a cluster-based routing protocol with a high transmission efficiency to the base station. In this paper, we propose an energy consumption model for LEACH and compare it with the existing LEACH, advanced LEACH (ALEACH), and power-efficient gathering in sensor information systems (PEGASIS) algorithms in terms of network lifetime. The energy consumption model comprises energy-sensitive cluster formation and a cluster head selection technique. The setup and steady-state phases of the proposed model are discussed based on the cluster head selection. The simulation results demonstrated that a low-energy-consumption network was introduced, modeled, and validated for LEACH.

• Journal of Internet Technology
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• v.20 no.4
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• pp.1157-1166
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• 2019

A Wireless Body Area Network (WBAN) allows the seamless integration of miniaturized sensor nodes in or around a human body, which may cause damage to the surrounding body issue due to high temperature. Although various temperature aware routing protocols have been proposed to prevent temperature rise of sensor nodes, most of them accommodate single traffic transmission with no mobility support. We propose a Forwarder based Temperature Aware Routing Protocol (FTAR) that supports multiple traffic transmission for normal and critical data. Normal data is forwarded directly to the sink through forwarding nodes which are selected among mobile nodes attached to the arms and legs, while critical data is forwarded to the sink through static nodes attached to fixed body parts with no mobility. We conduct extensive simulations of FTAR, and conclude that FTAR has good performance in terms of hot spot generation ratio, hot spot duration time, and packet delivery ratio.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.1-18
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• 2016

Owing to the extensive use of Web media and the development of the IT industry, a large amount of data has been generated, shared, and stored. Nowadays, various types of unstructured data such as image, sound, video, and text are distributed through Web media. Therefore, many attempts have been made in recent years to discover new value through an analysis of these unstructured data. Among these types of unstructured data, text is recognized as the most representative method for users to express and share their opinions on the Web. In this sense, demand for obtaining new insights through text analysis is steadily increasing. Accordingly, text mining is increasingly being used for different purposes in various fields. In particular, issue tracking is being widely studied not only in the academic world but also in industries because it can be used to extract various issues from text such as news, (SocialNetworkServices) to analyze the trends of these issues. Conventionally, issue tracking is used to identify major issues sustained over a long period of time through topic modeling and to analyze the detailed distribution of documents involved in each issue. However, because conventional issue tracking assumes that the content composing each issue does not change throughout the entire tracking period, it cannot represent the dynamic mutation process of detailed issues that can be created, merged, divided, and deleted between these periods. Moreover, because only keywords that appear consistently throughout the entire period can be derived as issue keywords, concrete issue keywords such as "nuclear test" and "separated families" may be concealed by more general issue keywords such as "North Korea" in an analysis over a long period of time. This implies that many meaningful but short-lived issues cannot be discovered by conventional issue tracking. Note that detailed keywords are preferable to general keywords because the former can be clues for providing actionable strategies. To overcome these limitations, we performed an independent analysis on the documents of each detailed period. We generated an issue flow diagram based on the similarity of each issue between two consecutive periods. The issue transition pattern among categories was analyzed by using the category information of each document. In this study, we then applied the proposed methodology to a real case of 53,739 news articles. We derived an issue flow diagram from the articles. We then proposed the following useful application scenarios for the issue flow diagram presented in the experiment section. First, we can identify an issue that actively appears during a certain period and promptly disappears in the next period. Second, the preceding and following issues of a particular issue can be easily discovered from the issue flow diagram. This implies that our methodology can be used to discover the association between inter-period issues. Finally, an interesting pattern of one-way and two-way transitions was discovered by analyzing the transition patterns of issues through category analysis. Thus, we discovered that a pair of mutually similar categories induces two-way transitions. In contrast, one-way transitions can be recognized as an indicator that issues in a certain category tend to be influenced by other issues in another category. For practical application of the proposed methodology, high-quality word and stop word dictionaries need to be constructed. In addition, not only the number of documents but also additional meta-information such as the read counts, written time, and comments of documents should be analyzed. A rigorous performance evaluation or validation of the proposed methodology should be performed in future works.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.137-147
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• 2011

In 2009, United Nations Educational, Scientific and Cultural Organization (UNESCO) recognized the unique outstanding ecosystem biodiversity and distinct ecocultural values of the Shinan Dadohae Biosphere Reserve in the island region. The Dadohae area, which has been sustainably conserved for scores of years, boasts not only a unique ecosystem, but also has residents with a wide range of traditional ecological knowledge. In terms of understanding the soundness of the ecosystem network known as the landscape system, the recent expansion of environmental development has served to heighten the degree of consideration given not only to biodiversity, which has long been used as an indicator to assess ecosystem soundness, but also to assess cultural diversity. Man has used the surrounding landscape and living organisms as his life resources since the beginning. Moreover, whenever necessary, man has developed new species through cultivation. Biodiversity became a foundation that facilitated establishing cultural diversity such as food and housing. Such ecological knowledge has been conveyed not only to adjacent regions, but also at the international level. The recent rapid changes in the Dadohae area island ecosystem caused by the transformation of fishing grounds by such factors as climate change, excess human activities, and marine pollution, is an epoch event in environmental history that shows that the balance between man and nature has become skewed. Furthermore, this issue has moved beyond the biodiversity and landscape diversity level to become an issue that should be addressed at the cultural diversity level. To this end, the time has come to pay close attention to this issue.