• Journal of Digital Convergence
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• v.18 no.10
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• pp.233-238
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• 2020

In the cloud environment, IoT devices using sensors and wearable devices are being applied in various environments, and technologies that accurately determine the information generated by IoT devices are being actively studied. However, due to limitations in the IoT environment such as power and security, information generated by IoT devices is very weak, so financial damage and human casualties are increasing. To accurately collect and analyze IoT information, this paper proposes a topographic information-based key management technique that considers IoT information errors. The proposed technique allows IoT layout errors and groups topographic information into groups of dogs in order to secure connectivity of IoT devices in the event of arbitrary deployment of IoT devices in the cloud environment. In particular, each grouped terrain information is assigned random selected keys from the entire key pool, and the key of the terrain information contained in the IoT information and the probability-high key values are secured with the connectivity of the IoT device. In particular, the proposed technique can reduce information errors about IoT devices because the key of IoT terrain information is extracted by seed using probabilistic deep learning.

• The KIPS Transactions:PartA
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• v.12A no.6 s.96
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• pp.539-546
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• 2005

Recently, as the number of Internet users are growing explosively, e-learning has been applied spread, as well as remote evaluation of intellectual capacity However, only the multiple choice and/or the objective tests have been applied to the e-learning, because of difficulty of natural language processing. For the intelligent marking of short-essay typed answer papers with rapidness and fairness, this work utilize heterogenous linguistic knowledges. Firstly, we construct the semantic kernel from un tagged corpus. Then the answer papers of students and instructors are transformed into the vector form. Finally, we evaluate the similarity between the papers by using the semantic kernel and decide whether the answer paper is correct or not, based on the similarity values. For the construction of the semantic kernel, we used latent semantic analysis based on the vector space model. Further we try to reduce the problem of information shortage, by integrating Korean Word Net. For the construction of the semantic kernel we collected 38,727 newspaper articles and extracted 75,175 indexed terms. In the experiment, about 0.894 correlation coefficient value, between the marking results from this system and the human instructors, was acquired.

• Cartoon and Animation Studies
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• s.19
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• pp.145-159
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• 2010

Similar to the other cultural contents, the character industry is based on the media which acts as the technological background. In fact, the character industry is the process of that a created character accesses to the consumers via media, builds its value and becomes licensed as a brand in the market. Therefore, it is crucial to select the most effective media for the consistence of a character in the market, as well as for construction of a higher brand quality of the character. Today, "Webtoon" might be considered as one of the marketing means which utilizes the Internet media for raising the character as a brand. Webtoon has apparent strength because it can be produced in shorter period and with less expense than through other media. Furthermore, Webtoon can be simply featured by the easiness of two-way communication and transference to another media through it. For these reasons, and according to the result of analyzing some Korean Webtoons, it seems obvious that the most effective media in character marketing is the Internet. In addition to the Internet, the strategic development in the media-mix is also important for establishing a brand of a character. However, the effective media-mix is available only when the character's external identity meets with the trait of its media. For the purpose of learning how the media-mix works when a character reaches for the consumers, a character "Hamataeng" was born and used in the experiment. This study will explain the marketing process through the use of own-created Webtoon and other contents, and suggest the ways to build a brand of a character. In addition, it is also indicated that a media-mixing strategy for transformation and expansion of the character to other media.

• Korean Parent-Child Health Journal
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• v.4 no.2
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• pp.56-72
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• 2002

Web sites on the internet are getting major resources to gain information related to child care. Though the numerous web sites deliver child care information, they have never been evaluated with criteria before. The purpose of this study is to identify existence and organization of child care portal sites and to analysis their contents, therefore to suggest guidelines for parents. The survey was conducted from Sep. 1. to Oct. 30., 2001. by means of Lycos Korea and Daum search engine and finally 45 portal sites related to child care were selected eliminating the commercial and personal homepages. The results were as follows: 1. Most of the sites(95.6%) were operated by corporations without registration(82.2%). Consultants were mostly professional (71.1%). 2. The contents were analyzed 4 categories including 19 themes. 3. Diet & Nutrition category include weaning food, breast feeding, bottle feeding and snack. The recipe and type of weaning food on months were topped(64.44%). 4. Infant Care category include bathing, sleeping, clothes, skin care and cord care. bathing method and heat of bathing water were topped(44.44%). 5. Growth & Development category was consisted of developmental characteristics, dental growth, play & exercise and learning & guidance. Developmental stage and motor development were topped (62.22%). 6. Health maintenance & promotion category was consisted of emergency care, prevention of accidents, vaccination, common pediatric disease and parental role. Care of diarrhea, constipation and fever, and precautions for vaccination were topped(48.89%).

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.145-154
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• 2022

Automatic Target Recognition (ATR) technology is emerging as a core technology of Future Combat Systems (FCS). Conventional ATR is performed based on IMINT (image information) collected from the SAR sensor, and various image-based deep learning models are used. However, with the development of IT and sensing technology, even though data/information related to ATR is expanding to HUMINT (human information) and SIGINT (signal information), ATR still contains image oriented IMINT data only is being used. In complex and diversified battlefield situations, it is difficult to guarantee high-level ATR accuracy and generalization performance with image data alone. Therefore, we propose a knowledge graph-based ATR method that can utilize image and text data simultaneously in this paper. The main idea of the knowledge graph and deep model-based ATR method is to convert the ATR image and text into graphs according to the characteristics of each data, align it to the knowledge graph, and connect the heterogeneous ATR data through the knowledge graph. In order to convert the ATR image into a graph, an object-tag graph consisting of object tags as nodes is generated from the image by using the pre-trained image object recognition model and the vocabulary of the knowledge graph. On the other hand, the ATR text uses the pre-trained language model, TF-IDF, co-occurrence word graph, and the vocabulary of knowledge graph to generate a word graph composed of nodes with key vocabulary for the ATR. The generated two types of graphs are connected to the knowledge graph using the entity alignment model for improvement of the ATR performance from images and texts. To prove the superiority of the proposed method, 227 documents from web documents and 61,714 RDF triples from dbpedia were collected, and comparison experiments were performed on precision, recall, and f1-score in a perspective of the entity alignment..

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.11
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• pp.46-52
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• 2017

Along with the expansion of areas in which ICT and Internet of Things (IoT) devices are utilized, open source software has recently expanded its scope of applications to include computers, smart phones, and IoT devices. Hence, as the scope of open source software applications has varied, there have been increasing malicious attempts to attack the weaknesses of open source software. In order to address this issue, various secure coding programs have been developed. Nevertheless, numerous vulnerabilities are still left unhandled. This paper provides some methods to handle newly raised weaknesses based on the analysis of histories and patterns of previous open source vulnerabilities. Through this study, we have designed a weaknesses analysis system that utilizes weakness histories and pattern learning, and we tested the performance of the system by implementing a prototype model. For five vulnerability categories, the average vulnerability detection time was shortened by about 1.61 sec, and the average detection accuracy was improved by 44%. This paper can provide help for researchers studying the areas of weaknesses analysis and for developers utilizing secure coding for weaknesses analysis.

• Journal of The Korean Association For Science Education
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• v.36 no.2
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• pp.325-335
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• 2016

Software (SW) education is guided by the government to operate not only computer subject matter but also related subject matter. SW education is highlighted in the 2015 Revised Curriculum and Guide for Operating SW Education. SW education is related with science education. For example, education on algorithms employing SW and activities using sensors/output control can be an effective strategy for scientific inquiry. The method can also be applied in developing Computational Thinking (CT) in students. In this study, we designed lessons to solve everyday scientific problems using Educational Programming Language (EPL) SW and physical computing materials and applied them to high school students. We conducted surveys that were modified from questionnaires of Internet application capability and based on the standard of accomplishment of SW education as well as elements of CT to find out the change in perceptions on programming and CT of students. We also conducted a survey on students' attitude toward science learning after an SW inquiry activity. In the results, perceptions on programming and CT of students were improved through lessons using unplugged activity, EPL SW, and physical computing. In addition, scores for interest, self-directed learning ability, and task commitment were high.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.91-107
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• 2012

As the Internet becomes more popular, many people use it to communicate. With the increasing number of personal homepages, blogs, and social network services, people often expose their personal information online. Although the necessity of those services cannot be denied, we should be concerned about the negative aspects such as personal information leakage. Because it is impossible to review all of the past records posted by all of the people, an automatic personal information detection method is strongly required. This study proposes a method to detect or classify online documents that contain personal information by analyzing features that are common to personal information related documents and learning that information based on the Na$\ddot{i}$ve Bayes algorithm. To select the document classification algorithm, the Na$\ddot{i}$ve Bayes classification algorithm was compared with the Vector Space classification algorithm. The result showed that Na$\ddot{i}$ve Bayes reveals more excellent precision, recall, F-measure, and accuracy than Vector Space does. However, the measurement level of the Na$\ddot{i}$ve Bayes classification algorithm is still insufficient to apply to the real world. Lewis, a learning algorithm researcher, states that it is important to improve the quality of category features while applying learning algorithms to some specific domain. He proposes a way to incrementally add features that are dependent on related documents and in a step-wise manner. In another experiment, the algorithm learns the additional dependent features thereby reducing the noise of the features. As a result, the latter experiment shows better performance in terms of measurement than the former experiment does.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.3
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• pp.59-70
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• 2021

With the recent advancements in the Internet of Things, context-aware system that provides customized services become important to consider. The existing context-aware systems analyze data generated around the user and abstract the context information that expresses the state of situations. However, these datasets is mostly unstructured and have difficulty in processing with simple approaches. Therefore, providing context-aware services using the datasets should be managed in simplified method. One of examples that should be considered as the unstructured datasets is a deep learning application. Processes in deep learning applications have a strong coupling in a way of abstracting dataset from the acquisition to analysis phases, it has less flexible when the target analysis model or applications are modified in functional scalability. Therefore, an abstraction model that separates the phases and process the unstructured dataset for analysis is proposed. The proposed abstraction utilizes a description name Analysis Model Description Language(AMDL) to deploy the analysis phases by each fat client is a specifically designed instance for resource-oriented tasks in edge computing environments how to handle different analysis applications and its factors using the AMDL and Fat client profiles. The experiment shows functional scalability through examples of AMDL and Fat client profiles targeting a vehicle image recognition model for vehicle access control notification service, and conducts process-by-process monitoring for collection-preprocessing-analysis of unstructured data.

• The Journal of the Korea Contents Association
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• v.22 no.7
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• pp.55-62
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• 2022

The perovskite solar cell is an active part of research in renewable energy fields such as solar energy, wind, hydroelectric power, marine energy, bioenergy, and hydrogen energy to replace fossil fuels such as oil, coal, and natural gas, which will gradually disappear as power demand increases due to the increase in use of the Internet of Things and Virtual environments due to the 4th industrial revolution. The perovskite solar cell is a solar cell device using an organic-inorganic hybrid material having a perovskite structure, and has advantages of replacing existing silicon solar cells with high efficiency, low cost solutions, and low temperature processes. In order to optimize the light absorption layer thin film predicted by the existing empirical method, reliability must be verified through device characteristics evaluation. However, since it costs a lot to evaluate the characteristics of the light-absorbing layer thin film device, the number of tests is limited. In order to solve this problem, the development and applicability of a clear and valid model using machine learning or artificial intelligence model as an auxiliary means for optimizing the light absorption layer thin film are considered infinite. In this study, to estimate the light absorption layer thin-film optimization of perovskite solar cells, the regression models of the support vector machine's linear kernel, R.B.F kernel, polynomial kernel, and sigmoid kernel were compared to verify the accuracy difference for each kernel function.