• Journal of Information Science Theory and Practice
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• v.9 no.2
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• pp.66-82
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• 2021

Recommender Systems have gained immense popularity due to their capability of dealing with a massive amount of information in various domains. They are considered information filtering systems that make predictions or recommendations to users based on their interests and preferences. The more recent technology, Linked Open Data (LOD), has been introduced, and a vast amount of Resource Description Framework data have been published in freely accessible datasets. These datasets are connected to form the so-called LOD cloud. The need for semantic data representation has been identified as one of the next challenges in Recommender Systems. In a LOD-enabled recommendation framework where domain awareness plays a key role, the semantic information provided in the LOD can be exploited. However, dealing with a big chunk of the data from the LOD cloud and its integration with any domain datasets remains a challenge due to various issues, such as resource constraints and broken links. This paper presents the challenges of interconnecting and extracting the DBpedia data with the MovieLens 1 Million dataset. This study demonstrates how LOD can be a vital yet rich source of content knowledge that helps recommender systems address the issues of data sparsity and insufficient content analysis. Based on the challenges, we proposed a few alternatives and solutions to some of the challenges.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1567-1573
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• 2020

The development of Internet technology has brought the era of one-man media. An individual produces content on user own and uploads it to related online services, and many users watch the content of online services using devices that allow them to use the Internet. Currently, most users find and watch content they want through search functions provided by existing online services. These features are provided based on information entered by the user who uploaded the content. In an environment where content needs to be retrieved based on these limited word data, user unwanted information is presented to users in the search results. To solve this problem, in this paper, the system actively analyzes the video in the online service, and presents a way to extract and reflect the characteristics held by the video. The research was conducted to extract morphemes based on the story content based on the voice data of a video and analyze them with big data technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.172-178
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• 2020

In order to recommend contents, the company generally uses collaborative filtering that takes into account both user preferences and video (item) similarities. Such services are primarily intended to facilitate user convenience by leveraging personal preferences such as user search keywords and viewing time. It will also be ranked around the keywords specified in the video. However, there is a limit to analyzing video similarities using limited keywords. In such cases, the problem becomes serious if the specified keyword does not properly reflect the item. In this paper, I would like to propose a system that identifies the characteristics of a video as it is by the system without human intervention, and analyzes and recommends similarities between videos. The proposed system analyzes similarities by taking into account all words (keywords) that have different meanings from training videos, and in such cases, the methods handled by big data clusters are applied because of the large scale of data and operations.

• Journal of Advanced Navigation Technology
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• v.22 no.2
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• pp.96-104
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• 2018

NOTAM is an announcement that is distributed to flight attendants with status information related to aviation. ICAO, the International Civilian Aviation Organization, recommends that a NOTAM service be provided for the SBAS service in order to use the SBAS signal-based access procedure. To comply with ICAO recommendation, KASS must provide NOTAM service to all aircraft landing using SBAS signal in order to provide APV-I SoL service. Therefore, it is necessary to develop KASS NOTAM system to provide KASS NOTAM service. In this paper, we analyzed the regulations related to NOTAM in Korea and abroad and analyzed the present state of NOTAM service in Korea. Based on this, we propose a method of providing KASS NOTAM service. We analyzed the NOTAM system of WAAS in the US and EGNOS in Europe and analyzed the main functional requirements of the KASS NOTAM system.

• The Journal of Korean Institute of Information Technology
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• v.17 no.3
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• pp.31-41
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• 2019

In this paper, we design and implement the simulator for the transmitter and receiver of 4D-8PSK TCM with 2.0, 2.25, 2.5, and 2.75 bits/symbol-channel transmission efficiency recommended by the CCSDS for satellite communications, and then analyze the BER performance of 4D-8PSK TCM system in AWGN channel. The transmitter of 4D-8PSK TCM is designed in accordance with the recommendation in the CCSDS standard. Meanwhile, for the receiver design of 4D-8PSK TCM, we design the differential decoder generalizing the differential encoder/decoder scheme. The trellis decoding algorithm is designed by applying the auxiliary trellis information and the Viterbi algorithm, and an 8-dimensional constellation mapper equation given in the CCSDS standard is deconstructed to design constellation mapper. Especially, we present the optimized receiver for 4D-8PSK TCM system by investigating the BER performances for the traceback lengths in the Viterbi decoder through computer simulations..

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.89-96
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• 2021

The number of confirmed cases of coronavirus-19 is soaring around the world and has caused numerous deaths. Wearing a mask is very important to prevent infection. Incidents and accidents have occurred due to the recommendation to wear a mask in public places such as buses and subways, and it has emerged as a serious social problem. To solve this problem, this paper proposes an open source-based face mask wearing monitoring system. We used open source software, web-based artificial intelligence tool teachable machine and open source hardware Arduino. It judges whether the mask is worn, and performs commands such as guidance messages and alarms. The learning parameters of the teachable machine were learned with the optimal values of 50 learning times, 32 batch sizes, and 0.001 learning rate, resulting in an accuracy of 1 and a learning error of 0.003. We designed and implemented a mask wearing monitoring system that can perform commands such as guidance messages and alarms by determining whether to wear a mask using a web-based artificial intelligence tool teachable machine and Arduino to prove its validity.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.5
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• pp.1-11
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• 2022

With the development of deep learning technology, various research and development are underway to estimate preference rankings through learning, and it is used in various fields such as web search, gene classification, recommendation system, and image search. Approximation algorithms are used to estimate deep learning-based preference ranking, which builds more than k comparison sets on all comparison targets to ensure proper accuracy, and how to build comparison sets affects learning. In this paper, we propose a k-disjoint comparison set generation algorithm and a k-chain comparison set generation algorithm, a novel algorithm for generating paired comparison sets for crowd-sourcing-based deep learning affinity measurements. In particular, the experiment confirmed that the k-chaining algorithm, like the conventional circular generation algorithm, also has a random nature that can support stable preference evaluation while ensuring connectivity between data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.9-16
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• 2023

In the IoT environment, various objects mutually interactive, and various services can be composed based on this environment. In the previous study, we have developed a resource collaboration system to provide services by substituting limited resources in the user's personal device using resource collaboration. However, in the preceding system, when the number of resources and situations increases, the inference time increases exponentially. To solve this problem, this study proposes a method of classifying users and resources by applying the BIG5 user type classification model. In this paper, we propose a method to reduce the inference time by filtering the user's preferred resources through BIG5 type-based preprocessing and using the filtered resources as an input to the recommendation system. We implement the proposed method as a prototype system and show the validation of our approach through performance and user satisfaction evaluation.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.4
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• pp.729-738
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• 2022

Existing wardrobes have been used only for storing simple clothes. Since it has a function to store clothes, there is only one way to control the environment such as humidity or temperature, and there is only one way to purchase and store items such as a desiccant. In this paper, by increasing the convenience in the existing wardrobe, automatic temperature and humidity control and various convenient functions were added. In line with the smart home market and smart phone application market that have grown over the past several years, along with the development of a wardrobe with sensors, the temperature and humidity control function and other functions inside the wardrobe through Bluetooth pairing between the wardrobe and the smartphone can be customized to the user using a smartphone. Through the clothing selection function and the weather data in the application, we want to implement convenient functions such as the function of recommending clothes in the closet to match the weather.