• Journal of Information Technology Services
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• v.17 no.1
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• pp.135-153
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• 2018

E-Card Service for Child Nutrition Program is one of the main public policy services nowadays. In case of inconvenience during the use of the e-cards, it is recommended to cooperate with related organizations in order to promptly handle and provide guidance, and thoroughly manage child feeding service such as hygiene, nutrition and kindness etc. To do so, it is very important to provide food service that meets local actual conditions and children's needs in a cost effective manner for the underage who are worried about the poorly-fed by understanding the pattern of child feeding e-card service. Hence. this paper aims to investigate how child feeding e-card service efficiently provides meals according to the local situation and children's needs through big data analysis and to propose a method of identifying welfare conditions according to the purpose of service with actual application examples. The results suggest that, first of all, this study is able to judge appropriateness of public institution's policy in a timely and repetitive manner through non-standard data analysis such as Naver News and transaction data. Secondly, this paper proposes a multi-layered analysis framework, which performs online open data analysis to detect policy issues, visualizes retrieval and preprocessing of real data, and performs abnormal pattern recognition. These will be worthy of reference to other similar projects.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2118-2126
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• 2017

The large-scale power system blackouts have indicated that conventional protection relays that based on local signals cannot fit for modern power grids with complicated setting or heavily loaded-flow transfer. In order to accurately detect various faulted lines and improve the fault-tolerance of wide-area protection, a novel multi-section weighed fault matching and detecting algorithm is proposed. The real protection vector (RPV) and expected section protection vectors (ESPVs) for five fault sections are constructed respectively. The function of multi-section weighed fault matching is established to calculate the section fault matching degrees between RPV and five ESPVs. Then the fault degree of protected line based on five section fault degrees can be obtained. Two fault detecting criterions are given to support the higher accuracy rate of detecting fault. With the enumerating method, the simulation tests illustrate the correctness and fault-tolerance of proposed algorithm. It can reach the target of 100% accuracy rate under 5 bits error of wide-area protections. The influence factors of fault-tolerance are analyzed, which include the choosing of wide-area protections, as well as the topological structures of power grid and fault threshold.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.4
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• pp.723-730
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• 2017

In this paper, we consider an online compensation algorithm considering the mutual coupling and suggest a new GPS antenna array to apply. To evaluate the anti-jamming performance for the proposed antenna array, ULA and URA, we divide direction finding of multiple jamming signals into environments. 1. there is no mutual coupling. 2. there is mutual coupling but no compensation. 3. mutual coupling is compensated. RMSE analysis showed that the online compensation algorithm works and that peak detection is possible for multiple jamming signals.

• Journal of Convergence for Information Technology
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• v.9 no.9
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• pp.20-26
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• 2019

With the development of ICT technology and the promotion of smartphone penetration, purchasing services that purchase various products through online market are being activated. In particular, due to advances in storage and delivery technology, sales of short food materials can be purchased online. Therefore, in this paper, we propose an integrated solution that enables advertisement effect, ordering and delivery through a purchase service even if there is no sales knowledge and sales network in a small shopping mall where only offline sales can be performed. The proposed system is able to efficiently view the product information by category through image search for the product that the user desires, so that the seller of the registered product can efficiently sell without any additional advertisement.

• International Journal of Computer Science & Network Security
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• v.23 no.9
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• pp.192-198
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• 2023

Cyberbullying is a growing problem among adolescents and can have serious psychological and emotional consequences for the victims. In recent years, machine learning techniques have emerged as promising approach for detecting instances of cyberbullying in online communication. This research paper focuses on developing a machine learning models that are able to detect cyberbullying including support vector machines, naïve bayes, and random forests. The study uses a dataset of real-world examples of cyberbullying collected from Twitter and extracts features that represents the ideational metafunction, then evaluates the performance of each algorithm before and after considering the theory of systemic functional linguistics in terms of precision, recall, and F1-score. The result indicates that all three algorithms are effective at detecting cyberbullying with 92% for naïve bayes and an accuracy of 93% for both SVM and random forests. However, the study also highlights the challenges of accurately detecting cyberbullying, particularly given the nuanced and context-dependent nature of online communication. This paper concludes by discussing the implications of these findings for future research and the development of practical tool for cyberbullying prevention and intervention.

• Smart Structures and Systems
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• v.33 no.3
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• pp.227-241
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• 2024

Safety and structural integrity of civil structures, like bridges and buildings, can be substantially enhanced by employing appropriate structural health monitoring (SHM) techniques for timely diagnosis of incipient damages. The information gathered from health monitoring of important infrastructure helps in making informed decisions on their maintenance. This ensures smooth, uninterrupted operation of the civil infrastructure and also cuts down the overall maintenance cost. With an early warning system, SHM can protect human life during major structural failures. A real-time online damage localization technique is proposed using only the vibration measurements in this paper. The concept of the 'Degree of Scatter' (DoS) of the vibration measurements is used to generate a spatial profile, and fractal dimension theory is used for damage detection and localization in the proposed two-phase algorithm. Further, it ensures robustness against environmental and operational variability (EoV). The proposed method works only with output-only responses and does not require correlated finite element models. Investigations are carried out to test the presented algorithm, using the synthetic data generated from a simply supported beam, a 25-storey shear building model, and also experimental data obtained from the lab-level experiments on a steel I-beam and a ten-storey framed structure. The investigations suggest that the proposed damage localization algorithm is capable of isolating the influence of the confounding factors associated with EoV while detecting and localizing damage even with noisy measurements.

• KIISE Transactions on Computing Practices
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• v.22 no.11
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• pp.614-618
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• 2016

As information on the internet and the data from smart devices are growing, the amount of stream data is also increasing in the real world. The stream data, which is a potentially large data, requires online learnable models and algorithms. In this paper, we propose a novel class of models: predictive convolutional neural networks to be able to perform online learning. These models are designed to deal with longer patterns as the layers become higher due to layering convolutional operations: detection and max-pooling on the time axis. As a preliminary check of the concept, we chose two-month gathered GPS data sequence as an observation sequence. On learning them with the proposed method, we compared the original sequence and the regenerated sequence from the abstract information of the models. The result shows that the models can encode long-range patterns, and can generate a raw observation sequence within a low error.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.25-31
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• 2018

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.

• The Journal of Society for e-Business Studies
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• v.12 no.3
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• pp.75-93
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• 2007

This paper presents real-time online early fire warning systems developed for preserving cultural properties of Bulguksa which is a world heritage designated by UNESCO. The system is based on the ubiquitous sensor network employing 900MHz and 2.4GHz bands. In this paper, we analyze requirements that should be considered in building effective management systems of cultural heritages by using wireless sensor network. Finally, we introduce the architecture, sensor and network design, and software design of the fire warning systems which is an initial version of U-Bulguksa. The current version of systems has been operating in Bukguksa for a few months. U-Bukguksa project sponsored by National Information Society Agency is ultimately aimed at developing an integrated system of U-cultural heritage management and U-tourism. The former aims to conserve and manage intangible cultural properties by providing a variety of environmental information such as erosion, crack, and gradient as well as fire which are important causes of loss and damage in real-time and online. The latter refers to the intelligent tourism information and guidance systems allowing tourists to get the personalized content on cultural heritages and help guidance with mobile devices in Bulguksa.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.157-163
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• 2022

As the online game market grows, the use of game bots is causing the most serious problem for game services. We propose a harvest coordinate analysis model to detect harvesting bots among game bots of the Massively Multiplayer Online Role-Playing Games(MMORPGs) genre. The proposed model analyzes the player's harvesting behavior using the coordinate data. Game bots can obtain in-game goods and items more easily than normal players and are not affected by realistic restrictions such as sleep time and character manipulation fatigue. As a result, there is a difference in harvesting coordinates between normal players and game bots. We divided the coordinate zones and used these coordinate zone differences to distinguish between game bot players and normal players. We created a dataset with NCSoft's AION log and applied it to a random forest model to detect game bots, and as a result, we derived performance with a recall of 0.72 and a precision of 0.92.