• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.99-104
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• 2021

In this paper, we propose health risk management using feature extraction and cluster analysis considering time flow. The proposed method proceeds in three steps. The first is the pre-processing and feature extraction step. It collects user's lifelog using a wearable device, removes incomplete data, errors, noise, and contradictory data, and processes missing values. Then, for feature extraction, important variables are selected through principal component analysis, and data similar to the relationship between the data are classified through correlation coefficient and covariance. In order to analyze the features extracted from the lifelog, dynamic clustering is performed through the K-means algorithm in consideration of the passage of time. The new data is clustered through the similarity distance measurement method based on the increment of the sum of squared errors. Next is to extract information about the cluster by considering the passage of time. Therefore, using the health decision-making system through feature clusters, risks able to managed through factors such as physical characteristics, lifestyle habits, disease status, health care event occurrence risk, and predictability. The performance evaluation compares the proposed method using Precision, Recall, and F-measure with the fuzzy and kernel-based clustering. As a result of the evaluation, the proposed method is excellently evaluated. Therefore, through the proposed method, it is possible to accurately predict and appropriately manage the user's potential health risk by using the similarity with the patient.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.1
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• pp.19-26
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• 2021

The purpose of this study is to provide an environment that can support the development of the virtual asset industry. The limiting factors and countermeasures currently possessed by the virtual asset industry were considered in terms of legal and institutional aspects, technical aspects, and market aspects.Small businesses classified as virtual asset operators have difficulty meeting the government's requirements.Accordingly, SMEs with insufficient funds and manpower are withdrawn from the market, creating an environment where only large-scale enterprises with capital power survive.It is difficult to develop desirable technologies and markets in the virtual asset technology industry. In addition, small and medium-sized companies may be expelled from the market, causing damage to current users. Therefore, in terms of legal and institutional aspects, there is a lack of an exact scope of virtual asset providers, and thus it is necessary to respond to the controversial elements of virtual asset providers. In terms of technology, it is necessary to cope with the slowdown of the P2P method, the difficulty in recovering errors, and the absence of operational experts. Therefore, technology standardization and stabilization are required, and efforts must be made to cultivate operational technical personnel who can support them.In terms of the market, it is necessary to prepare measures to protect users of virtual assets and to establish countermeasures for companies operating virtual assets against weak user protection, inadequate application of the AML method, and limitations of taxation. This study is expected to contribute to active utilization support or related policies in the virtual asset industry.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.5
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• pp.131-142
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• 2009

While users of information security products require high-quality products that are secure and have high performance, there are neither examples for evaluating the quality of information security products nor studies on the quality model and metrics for the quality evaluation. In this paper, information security products are categorized into three different types and the security and performance of various information security products are analyzed. Through this process and after consideration of information security products' security and performance, a new quality model that possesses 7 characteristics and 24 sub-characteristics has been defined. In addition, metrics consisting of 62 common and 45 extended metrics that can be used to evaluate the quality of information security products are introduced, and a proposition for a method of generating the quality evaluation metrics for specific information security products is included. The method of generating metrics proposed in this paper can be extended in order to be applied to a variety of information security products, and by generating and verifying the quality evaluation metrics for firewall, intrusion detection systems and fingerprint systems it is shown that it applicable on a variety of information security products.

• Journal of Convergence for Information Technology
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• v.11 no.6
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• pp.33-39
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• 2021

Speech recognition technology is being combined with deep learning and is developing at a rapid pace. In particular, voice recognition services are connected to various devices such as artificial intelligence speakers, vehicle voice recognition, and smartphones, and voice recognition technology is being used in various places, not in specific areas of the industry. In this situation, research to meet high expectations for the technology is also being actively conducted. Among them, in the field of natural language processing (NLP), there is a need for research in the field of removing ambient noise or unnecessary voice signals that have a great influence on the speech recognition recognition rate. Many domestic and foreign companies are already using the latest AI technology for such research. Among them, research using a convolutional neural network algorithm (CNN) is being actively conducted. The purpose of this study is to determine the non-voice section from the user's speech section through the convolutional neural network. It collects the voice files (wav) of 5 speakers to generate learning data, and utilizes the convolutional neural network to determine the speech section and the non-voice section. A classification model for discriminating speech sections was created. Afterwards, an experiment was conducted to detect the non-speech section through the generated model, and as a result, an accuracy of 94% was obtained.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.268-279
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• 2021

The geospatial schemes based on topo-climatology have been developed to produce digital climate maps at a site-specific scale. Their development processes are reviewed here to derive the needs for new schemes in the future. Agricultural and forestry villages in Korea are characterized by complexity and diversity in topography, which results in considerably large spatial variations in weather and climate over a small area. Hence, the data collected at a mesoscale through the Automated Synoptic Observing System (ASOS) operated by the Korea Meteorological Administration (KMA) are of limited use. The geospatial schemes have been developed to estimate climate conditions at a local scale, e.g., 30 m, lowering the barriers to deal with the processes associated with production in agricultural and forestry industries. Rapid enhancement of computing technologies allows for near real-time production of climate information at a high-resolution even in small catchment areas and the application to future climate change scenarios. Recent establishment of the early warning service for agricultural weather disasters can provide growth progress and disaster forecasts for cultivated crops on a farm basis. The early warning system is being expanded worldwide, requiring further advancement in geospatial schemes and digital climate mapping.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.1
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• pp.56-61
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• 2022

In this paper, we classify and present AI algorithms and natural language processing methods used in chatbots. A framework that can be used to implement a chatbot is also described. A chatbot is a system with a structure that interprets the input string by constructing the user interface in a conversational manner and selects an appropriate answer to the input string from the learned data and outputs it. However, training is required to generate an appropriate set of answers to a question and hardware with considerable computational power is required. Therefore, there is a limit to the practice of not only developing companies but also students learning AI development. Currently, chatbots are replacing the existing traditional tasks, and a practice course to understand and implement the system is required. RNN and Char-CNN are used to increase the accuracy of answering questions by learning unstructured data by applying technologies such as deep learning beyond the level of responding only to standardized data. In order to implement a chatbot, it is necessary to understand such a theory. In addition, the students presented examples of implementation of the entire system by utilizing the methods that can be used for coding education and the platform where existing developers and students can implement chatbots.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.61-70
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• 2022

General users who use smartphone apps often use the Vault app to protect personal information such as photos and videos owned by individuals. However, there are increasing cases of criminals using the Vault app function for anti-forensic purposes to hide illegal videos. These apps are one of the apps registered on Google Play. This paper proposes a methodology for extracting feature points through XML-based keyword frequency analysis to explore Vault apps used by criminals, and text mining techniques are applied to extract feature points. In this paper, XML syntax was compared and analyzed using strings.xml files included in the app for 15 hidden Vault anti-forensics apps and non-hidden Vault apps, respectively. In hidden Vault anti-forensics apps, more hidden-related words are found at a higher frequency in the first and second rounds of terminology processing. Unlike most conventional methods of static analysis of APK files from an engineering point of view, this paper is meaningful in that it approached from a humanities and sociological point of view to find a feature of classifying anti-forensics apps. In conclusion, applying text mining techniques through XML parsing can be used as basic data for exploring hidden Vault anti-forensics apps.

• Journal of the Korea Institute of Building Construction
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• v.22 no.3
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• pp.281-291
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• 2022

This study attempts to use big data to determine the indicators necessary for a fire risk assessment of buildings. Because most of the causes affecting the fire risk of buildings are fixed as indicators considering only the building itself, previously only limited and subjective assessment has been performed. Therefore, if various internal and external indicators can be considered using big data, effective measures can be taken to reduce the fire risk of buildings. To collect the data necessary to determine indicators, a query language was first selected, and professional literature was collected in the form of unstructured data using a web crawling technique. To collect the words in the literature, pre-processing was performed such as user dictionary registration, duplicate literature, and stopwords. Then, through a review of previous research, words were classified into four components, and representative keywords related to risk were selected from each component. Risk-related indicators were collected through analysis of related words of representative keywords. By examining the indicators according to their selection criteria, 20 indicators could be determined. This research methodology indicates the applicability of big data analysis for establishing measures to reduce fire risk in buildings, and the determined risk indicators can be used as reference materials for assessment.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.49-55
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• 2022

In this paper, we propose an obstacle detection method that can operate robustly even in external environmental factors such as weather. In particular, we propose an obstacle detection system that can accurately inform dangerous situations in AR through DB-based feature matching and RANSAC-based multiplane method. Since the approach to detecting obstacles based on images obtained by RGB cameras relies on images, the feature detection according to lighting is inaccurate, and it becomes difficult to detect obstacles because they are affected by lighting, natural light, or weather. In addition, it causes a large error in detecting obstacles on a number of planes generated due to complex terrain. To alleviate this problem, this paper efficiently and accurately detects obstacles regardless of lighting through DB-based feature matching. In addition, a criterion for classifying feature points is newly calculated by normalizing multiple planes to a single plane through RANSAC. As a result, the proposed method can efficiently detect obstacles regardless of lighting, natural light, and weather, and it is expected that it can be used to secure user safety because it can reliably detect surfaces in high and low or other terrains. In the proposed method, most of the experimental results on mobile devices reliably recognized indoor/outdoor obstacles.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.325-335
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• 2021

Chronic diseases have been increasing recently as the average life expectancy of humans has been extended, and this trend has caused problems such as the widespread demand for health and rehabilitation services and rising medical costs. In order to solve this problem, the community-based rehabilitation has been developed and strengthened in Korea and gradually promoted since 2019. It is important to secure access to clients who want to use services to revitalize community-based rehabilitation. So in this study, as part of the community-based rehabilitation, intends to devise smartphone applications designs and develop a prototype to secure access to community-based occupational therapy services based on occupation and activities. For Occupational Therapy Practice Framework (OTPF), International Classification of Functioning, Disability and Health (ICF), and Allen Diagnostic Module-2 (ADM-2) were used to devise and categorize occupation and activity based application content, and link OTPF, ICF, and ADM-2 through prior research analysis and expert meetings. The derived content was visualized through literature review and activity analysis, and was implemented to enable direct playback within the application using the YouTube API, and finally developed a prototype application. The Android Studio 3.5.2 for Windows 64-bit was used to build the application prototype. In further research, converging various digital technologies for user convenience and additionally researching community-based occupational therapy service providers opinions and service user satisfaction will improve accessibility to community-based occupational therapy services for clients who have difficulty occupational performance in the community.