• Journal of the Korean Institute of Educational Facilities
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• v.6 no.1
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• pp.5-20
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• 1999

With the advent of internet, the modern society is enjoying the benefits of the information age. As one of undesirable side effects of utilization of internet, however, it is often mentioned that young students are helplessly exposed to inappropriate and unqualified information. Therefore, in this paper, we will clarify the nature of inappropriate information to the younger generation and will argue for the needs of protecting the youth from inappropriate information. Especially the merits and limits of often motioned five different protective and regulatory measures are presented and analyzed, those are, establishment of acceptable use policy, active utilization of supervisory organization, promotion of Internet rating system, installation of filtering software, and legal and regulatory protection. As a fundamental means of resolving the problems, however, enforcement of systematic information literacy education, promotion of active utilization of sound information, development of search engines for the youth, design of diverse filtering softwares which can be selected by users, and increased attention by parents and teachers are suggested.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.107-112
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• 2011

In this paper, we introduce the implementation of the security system using iris recognition. This system acquires images with infrared camera and extracts the 2D code from a infrared image which uses scale-space filtering and concavity. We examine the system by (i) extract 2D code and (ii) compare the code that stored on the server (iii) measure FAR and FRR using pattern matching. Experiment results show that the proposed method is very suitable.

• Journal of Internet Computing and Services
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• v.23 no.1
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• pp.125-134
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• 2022

The development of information communication and artificial intelligence technology requires the intelligent command and control (C2) system for Korean military, and various studies are attempted to achieve it. In particular, as a volume ofinformation in the C2 workflow increases exponentially, this study pays attention to the collaborative filtering (CF) and recommendation systems (RS) that can provide the essential information for the users of the C2 system has been developed. The RS performing information filtering in the C2 system should provide an explanatory recommendation and consider the context of the tasks and users. In this paper, we propose a contextual pre-filtering CARS framework that recommends information in the C2 workflow. The proposed framework consists of four components: 1) contextual pre-filtering that filters data in advance based on the context and relationship of the users, 2) feature selection to overcome the data sparseness that is a weak point for the CF, 3) the proposed CF with the features distances between the users used to calculate user similarity, and 4) rule-based post filtering to reflect user preferences. In order to evaluate the superiority of this study, various distance methods of the existing CF method were compared to the proposed framework with two experimental datasets in real-world. As a result of comparative experiments, it was shown that the proposed framework was superior in terms of MAE, MSE, and MSLE.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.2
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• pp.113-118
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• 2011

This paper has analyzed recognizing performance depending on the size of iris codes extracting by iris recognition algorithm using scale-space filtering. The iris images were created through pre-processing, the features were extracted by scale-space filtering, and the codes of 16 sizes were generated. The generated code's performance was compared for each code to calculate FAR and FRR by matching method utilizing Hamming distance. Every code had little overlapping portion between same person and other persons group so that the proposed algorithm's superiority was proved, and the performance of iris codes was analyzed for each size focused on convenience to use when implementing in realization. In addition, the iris codes suitable to iris recognition system that is high-reliable and is able to reduce user's inconvenience due to mis-rejection has been presented considering for commercialization.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.18-21
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• 2001

In this paper, some geometric condition for a stochastic nonlinear system and an adaptive control method for minimum-phase stochastic nonlinear system using neural network me provided. The state feedback linearization is widely used technique for excluding nonlinear terms in nonlinear system. However, in the stochastic environment, even if the minimum phase linear system derived by the feedback linearization is not sufficient to be controlled robustly. In the viewpoint of that, it is necessary to make an additional condition for observation of nonlinear stochastic system, called perfect filtering condition. In addition, on the above stochastic nonlinear observation condition, I propose an adaptive control law using neural network. Computer simulation shoo's that the stochastic nonlinear system satisfying perfect filtering condition is controllable and the proposed neural adaptive controller is more efficient than the conventional adaptive controller.

• International journal of advanced smart convergence
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• v.9 no.3
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• pp.42-48
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• 2020

These days people are overwhelmed by information on the Internet thus searching for useful information becomes burdensome, often failing to acquire some in a reasonable time. Recommender systems are indispensable to fulfill such user needs through many practical commercial sites. This study proposes a novel similarity measure for user-based collaborative filtering which is a most popular technique for recommender systems. Compared to existing similarity measures, the main advantages of the suggested measure are that it takes all the ratings given by users into account for computing similarity, thus relieving the inherent data sparsity problem and that it reflects the uncertainty or vagueness of user ratings through fuzzy logic. Performance of the proposed measure is examined by conducting extensive experiments. It is found that it demonstrates superiority over previous relevant measures in terms of major quality metrics.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.1-11
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• 2010

While a supply of GPS-enabled smartphone is increased, LBS which is studied and developed for special function is changed to personal solution. In this paper, we propose and implement on personalized method of individual LBS using collaborative filtering-based recommend system. Proposed personalized LBS system recommends contents which is expected to be interest for individual user, by predicting location-based contents within a user's setting radius. To evaluate performance of proposed system, we observed prediction accuracy with various experimental condition using our prototype. As a result, we confirmed that the convergence of collaborative filtering and LBS is effective for personalized LBS.

• 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.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.416-425
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• 2003

Recommender systems are a personalized information filtering technology to help customers find the products they would like to purchase. Collaborative filtering (CF) has been known to be the most successful recommendation technology. However its widespread use in e-commerce has exposed two research issues, sparsity and scalability. In this paper, we propose several hybrid recommender procedures based on web usage mining, clustering techniques and collaborative filtering to address these issues. Experimental evaluation of suggested procedures on real e-commerce data shows interesting relation between characteristics of procedures and diverse situations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.721-723
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• 2004

As the Internet use has been spreading worldwide, illegal and harmful contents have been increasing on the Internet, which has become a very serious social problem. To prevent children form exposing themselves to such illegal and harmful contents on the Internet, harmful information protection systems have been developed. We examine component technologies of harmful information protection systems including text and image-based filtering solutions as well as url-based filtering solution. Also we examine the related trends and strategies which effectively prevent access to the harmful contents.