• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.6
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• pp.259-268
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• 2018

The purpose of this study is to propose a training method customized to the level of cognitive abilities to increase users' interest and engagement while using cognitive function training contents. The level of cognitive ability of the users was based on the clustering based on the users' information and Mini-Mental Statue Examination-Korea Child test score using the K-means algorithm applied collaborative filtering. The results were applied to the integrated cognitive function training system, and the contents order and difficulty level of the cognitive function training area were recommended to the user's cognitive ability level. Particularly, the contents difficulty control was designed to give a high immersion feeling by applying the 'flow theory' method that users can repeatedly feel tension and comfort. In conclusion, the user-customized cognitive function training method proposed in this paper can be expected to be more effective and rehabilitative results than existing therapists' subjective setting of contents order and difficulty level.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.13 no.5
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• pp.169-177
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• 2003

We establish the security requirements and derive a generic condition of elliptic curve scalar multiplication to resist against DPA and Goubin’s attack. Also we show that if a scalar multiplication algorithm satisfies our generic condition, then both attacks are infeasible. Showing that the randomized signed scalar multiplication using Ha-Moon's receding algorithm satisfies the generic condition, we recommend the randomized signed scalar multiplication using Ha-Moon's receding algorithm to be protective against both attacks. Also we newly design a random recoding method to Prevent two attacks. Finally, in efficiency comparison, it is shown that the recommended method is a bit faster than Izu-Takagi’s method which uses Montgomery-ladder without computing y-coordinate combined with randomized projective coordinates and base point blinding or isogeny method. Moreover. Izu-Takagi’s method uses additional storage, but it is not the case of ours.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.173-178
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• 2022

Recently, a recommendation system using deep learning in social network services has been actively studied. However, in the case of a recommendation system using deep learning, the cold start problem and the increased learning time due to the complex computation exist as the disadvantage. In this paper, the user-tailored exercise routine recommendation algorithm is proposed using the user's metadata. Metadata (the user's height, weight, sex, etc.) set as the input of the model is applied to the designed model in the proposed algorithms. The exercise recommendation system model proposed in this paper is designed based on the neural collaborative filtering (NCF) algorithm using multi-layer perceptron and matrix factorization algorithm. The learning proceeds with proposed model by receiving user metadata and exercise information. The model where learning is completed provides recommendation score to the user when a specific exercise is set as the input of the model. As a result of the experiment, the proposed exercise recommendation system model showed 10% improvement in recommended performance and 50% reduction in learning time compared to the existing NCF model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.2A
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• pp.176-186
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• 2006

In this paper, we propose an enhanced symbol detector algorithm for 2.45GHz LR-WPAN(Low-Rate Wireless Personal Area Network) receiver. Because the frequency offset of $\pm$80ppm on 2.45GHz band is recommended in IEEE 802.15.4 LR-WPAN(Low-Rate Wireless Personal Area Network) specification, a symbol detector algorithm having stable operation in the channel environment with large frequency offset is required. For robustness to the frequency offset, non-coherent detection-based symbol detector algorithm is typically applied in the LR-WPAN receiver modem. However, the noncoherent symbol detector has increased performance degradation and hardware complexity due to squaring loss of I/Q squaring operation. Therefore we propose a coherent detection-based symbol detector algorithm with frequency offset compensation using a preamble symbol. The proposed algorithm is more suitable for LR-WPAN receiver aimed at low-cost, low-power and low-complexity than the non-coherent symbol detector, since it can reduce performance degradation due to squaring loss of I/Q squaring operation and implementation complexity. Simulation results show that the proposed algorithm has performance improvement of about 1dB in various channel environments.

• The Journal of Information Systems
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• v.27 no.4
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• pp.71-96
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• 2018

Purpose Recently, social interest in the convergence research is at its peak. However, contrary to the keen interest in convergence research, an infrastructure that makes it easier to recruit researchers from other fields is not yet well established, which is why researchers are having considerable difficulty in carrying out real convergence research. In this study, we implemented a researcher recommendation system that helps researchers who want to collaborate easily recruit researchers from other fields, and we expect it to serve as a springboard for growth in the convergence research field. Design/methodology/approach In this study, we implemented a system that recommends proper researchers when users enter keyword in the field of research that they want to collaborate using word embedding techniques, word2vec. In addition, we also implemented function of keyword suggestions by using keywords drawn from LDA Topicmodeling Algorithm. Finally, the UI of the researcher recommendation system was completed by utilizing the collaborative messenger Slack to facilitate immediate exchange of information with the recommended researchers and to accommodate various applications for collaboration. Findings In this study, we validated the completed researcher recommendation system by ensuring that the list of researchers recommended by entering a specific keyword is accurate and that words learned as a similar word with a particular researcher match the researcher's field of research. The results showed 85.89% accuracy in the former, and in the latter case, mostly, the words drawn as similar words were found to match the researcher's field of research, leading to excellent performance of the researcher recommendation system.

• Fashion & Textile Research Journal
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• v.21 no.5
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• pp.627-639
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• 2019

This study analyzes differences between the results of 3D direct measurements and automated measurements for Korean elderly females according to age groups, side somatotype, and BMI groups. This study compares the measurement differences of the direct and the 3D automated measurements for women between the ages of 70 to 85, according to age group, BMI group, and side somatotype. A comparison of the results of the direct measurement and the 3D automated measurements for elderly women show that a meaningful discrepancy exists for 29 items out of 33 items. Furthermore, the results of comparing the average error tolerance recommended by ISO20685 shows that 30 items out of 33 items exceeded ISO recommendations. The results of the automated measurement program shows a higher degree of accuracy for straight postures; however, this unsuitable for postures of elderly women with a changed somatotype. The analysis results of the measurement difference indicate the suitability of the automatic measurement programs is found to be high for stood postures, while problems seem to exist on several items along with an automated program is not appropriately used due to posture and part of body changes for elderly women. Therefore, it is recommended to develop an algorithm, that reflects the body changes of elderly women first and then upgrade the automated program equipped with a measurement size method. It is hoped that the study results can be utilized as base data for improving the automated measurement program.

• Journal of the Korean Society for information Management
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• v.38 no.2
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• pp.113-127
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• 2021

The purpose of this study is to propose a personalized book recommendation system to promote the use of university libraries. In particular, unlike many recommended services that are based on existing users' preferences, this study proposes a method that derive evaluation metrics using individual users' book rental history and tendencies, which can be an effective alternative when users' preferences are not available. This study suggests models using two matrix decomposition methods: Singular Value Decomposition(SVD) and Stochastic Gradient Descent(SGD) that recommend books to users in a way that yields an expected preference score for books that have not yet been read by them. In addition, the model was implemented using a user-based collaborative filtering algorithm by referring to book rental history of other users that have high similarities with the target user. Finally, user evaluation was conducted for the three models using the derived evaluation metrics. Each of the three models recommended five books to users who can either accept or reject the recommendations as the way to evaluate the models.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.151-166
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• 2020

A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

• Journal of Digital Convergence
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• v.12 no.6
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• pp.277-287
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• 2014

In LBSNS(Location-based Social Network Service), users can share locations and communicate with others by using check-in data. The check-in data consists of POI name, category, coordinate and address of locations, nickname of users, evaluating grade of locations, related article/photo/video, and etc. If you analyze the check-in data from the location-based social network service in accordance with your situation, you can provide various customized services. Therefore, In this paper, we develop a location recommendation system based on LBSNS that can utilize the check-in data efficiently. This system analyzes the location category of the check-in data, determines the weighted value of it, and finds out the similarity between users by using the Pearson correlation coefficient. Also, it obtains the preference score of recommended locations by using the collaborated filtering algorithm and then, finds out the distance score by applying the Euclidean's algorithm to the recommended locations and the current users' locations. Finally, it recommends appropriate locations by applying the weighted value to the preference score and the distance score. In addition, this paper approved excellence of the proposed system throughout the experiment using real data.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.56-66
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• 2022

Due to the recent increase in information encountered on social media, algorithm-based recommendation formats selectively provide information based on user information, which often causes a filter bubble effect by an Echo Chamber. Eco-chamber refers to a phenomenon in which beliefs are amplified or strengthened by communication only in an enclosed system, and filter bubbles refer to a phenomenon in which information providers provide customized information according to users' interests, and users encounter only filtered information. The purpose of this study is to propose a method of efficiently selecting information as a way to improve the filter bubble phenomenon by such an echo chamber. The research progress method analyzed recommended algorithms used on YouTube, Facebook and Amazon. In this study, humanities solutions such as training critical thinking skills of social media users and strengthening objective ethical standards according to self-preservation laws, and technical solutions of model-based cooperative filtering or cross-recommendation methods were presented. As a result, recommended algorithms should continue to supplement technology and develop new techniques, and humanities should make efforts to overcome cognitive dissonance and prevent users from falling into confirmation bias through critical thinking training and political communication education.