• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2086-2097
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• 2021

The current cultural assets are being restored depending on the opinions of experts (craftsmen). We intend to introduce digitalized artificial intelligence techniques, excluding the personal opinions of experts on reconstruction of such cultural properties. The first step toward restoring digitized cultural properties is separation. The restoration of cultural properties should be reorganized based on recorded documents, period historical backgrounds and regional characteristics. The cultural properties in the form of photographs or images should be collected by separating the background. In addition, when restoring cultural properties most of them depend a lot on the tendency of the restoring person workers. As a result, it often occurs when there is a problem in the accuracy and reliability of restoration of cultural properties. In this study, we propose a search method for learning stored digital cultural assets using AI technology. Pagoda was selected for restoration of Cultural Properties. Pagoda data collection was collected through the Internet and various historical records. The pagoda data was classified by period and region, and grouped into similar buildings. The collected data was learned by applying the well-known CNN algorithm for artificial intelligence learning. The pagoda search used Yolo Marker to mark the tower shape. The tower was used a total of about 100-10,000 pagoda data. In conclusion, it was confirmed that the probability of searching for a tower differs according to the number of pagoda pictures and the number of learning iterations. Finally, it was confirmed that the number of 500 towers and the epochs in training of 8000 times were good. If the test result exceeds 8,000 times, it becomes overfitting. All so, I found a phenomenon that the recognition rate drops when the enemy repeatedly learns more than 8,000 times. As a result of this study, it is believed that it will be helpful in data gathering to increase the accuracy of tower restoration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.3
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• pp.157-164
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• 2006

Information is playing a key role in sufficing the needs of individual members of the society in today's rapidly changing environment. Especially, the cases of illegal gathering of privacy information will increase and the leakage of privacy information will grow as the individual activities in the ubiquitous computing environment. In this paper, we suggested the privacy framework in order to make design and implementation of secure and effective privacy management system. Ant we also introduced the methodology which is represent to 5 specific stages in order to suggest to the privacy system development guideline from the standpoints of the privacy system operator or developer. Especially, we tried to determine whether the suggested methodology can be effectively used in the real computing environment or not by making necessary investments in management (privacy policy) and technical (system architecture) sides. We believe that the privacy framework and methodology introduced in this research can be utilized to suggest new approach for showing direction from the privacy protection perspective, which is becoming more important in ubiquitous environments, and practical application rather than providing conceptual explanation from the views of engineer or developer.

• The Korean Journal of Applied Statistics
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• v.34 no.1
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• pp.57-74
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• 2021

The proliferation of smart devices (such as smart phones and tablet PCs) has led to a marked increase in the use of mobile-based internet. As a result, the influence of the mobile internet has become important to make opinions on social issues. This study explores the effects of mobile internet news users' characteristics on formation of opinions about major political, economic and social issues. We used the data from the media audience awareness survey by the Korean Press Foundation in 2016 and 2017 in this analysis. The characteristics of the news users are gender, age, education, income, news usage days, news usage hours, media application usage days, news gathering application usage days, portal usage days, and media official website usage days. These characteristics are known as possible explanatory variables for the mobile internet news users. Multiple logistic regressions were done with interpretation to know which covariates affect on formation of major opinion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.11
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• pp.1414-1419
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• 2019

Patent indicators were used to analyze the movements of local industries in order to derive blank technologies due to rapid changes in technology in the 4th Industrial Revolution and to discover promising technologies. Currently, Changwon-si is gathering a lot of technical information to develop hydrogen electric vehicle technology as a future regional flagship industry to discover it as a promising technology in the future. Collecting technical information has many problems in terms of time and cost due to classification methods, technical trends, and similar technologies. Therefore, a systematic classification of technical information and a method for easily deriving technical trends are needed. In this paper, we analyzed the blank technology and promising technology trends for the future core industries of the region through the method of measuring the growth rate of patents and the frequency of patent application through the patent indicators.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.24 no.4
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• pp.1-14
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• 2021

This study suggested suitable soil textures that is proper to propagate native plants to manage and restore barren land in DMZ. Germination tests were conducted for 16 native herbaceous plants growing in the DMZ border area in accordance with FAO-BI (Biodiversity International) standards, and the germination rate and T₅₀ in vitro were investigated. In order to examine the germination characteristics according to the soil textures, we used gravel, bed and mixed soil and investigated the germination characteristics under ordinary room temperature conditions in the greenhouse. As a result, it was observed that the germination rate in the greenhouse was significantly decreased compared to the germination rate in vitro of the species advertised due to soil textures. T₅₀ between the in vitro and each soil texture showed significant differences whereas T₅₀ between soil textures alone did not in all species advertised. The germination rate in vitro of Aster koraiensis, Dendranthema zawadskii var. latilobum, Hosta clausa, and Hosta minor there was no significant difference compared to ordinary room temperature conditions. In addition, as the germination rate is demonstrated more than 70%, which is relatively higher than other species advertised, it is considered to have strong environmentally adaptable. On the other hand, considering that the 6 species of Leontopodium coreanum, Plantago major, Potentilla chinensis, Sedum kamtschaticum, Sedum latiovalifolium, and Veronica kiusiana demonstrated less than 50% of germination rate in vitro, it is expected to be difficult to propagate without pre-treatment. In order to use these 6 species as restoration material plants, it needs to be considered to pre-treat to improve germination rate, or to enhance the vitality of seeds by improving the seed gathering period and storage method.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.15-22
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• 2022

Modern technology drives the world, increasing performance while reducing labor and time expenses. Tanzania Atomic Energy Commission (TAEC) tracks employee's levels of exposure to radiation sources using dosimeters. According to legal compliance, workers wear dosimeters for three months and one month at the workplace. However, TAEC has problems in tracking, issuing and returning dosimeters because the existing tracking is done manually. The study intended to develop a Personal Dose Management System (PDMS) that processes and manages the data collected by dosimeters for easy and accurate records. During the requirements elicitation process, the study looked at the existing system. PDMS' requirement gathering included document reviews, user interviews, and focused group discussions. Development and testing of the system were implemented by applying the evolutionary prototyping technique. The system provides a login interface for system administrators, radiation officers, and Occupational Exposed Workers. The PDMS grants TAEC Staff access to monitor individual exposed workers, prints individual and institutional reports and manages workers' information. The system reminds the users when to return dosimeters to TAEC, generate reports, and facilitates dispatching and receiving dosimeters effectively. PDMS increases efficiency and effectiveness while minimizing workload, paperwork, and inaccurate records. Therefore, based on the results obtained from the system, it is recommended to use the system to improve dosimeter data management at the institution.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Archives of Plastic Surgery
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• v.49 no.4
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• pp.538-542
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• 2022

Performing the first peer review of a plastic surgical research article can be an overwhelming task. However, it is an essential scholarly skill and peer review is used in a multitude of settings: evaluation of journal articles, conference abstracts, and research proposals. Furthermore, peer reviewing provides more than just the opportunity to read and help improve other's work: peer reviewing can improve your own scientific writing. A structured approach is possible and recommended. In these ten tips, we provide guidance on how to successfully conduct the first peer reviews. The ten tips on peer reviewing concern: 1) Appropriateness: are you qualified and prepared to perform the peer review? 2) Familiarization with the journal and its reviewing guidelines; 3) Gathering first impressions of the paper followed by specific tips for reviewing; 4) the abstract and introduction; 5) Materials, methods, and results (including statistical considerations); and 6) discussion, conclusion, and references. Tip 7 concerns writing and structuring the review; Tips 7 and 8 describe how to provide constructive criticism and understanding the limits of your expertise. Finally, Tip 10 details why-and how-you become a peer reviewer. Peer review can be done by any plastic surgeon, not just those interested in an academic career. These ten tips provide useful insights for both the aspiring and the experienced peer reviewer. In conclusion, a systematic approach to peer reviewing is possible and recommended, and can help you getting started to provide quality peer reviews that contribute to moving the field of plastic surgery forward.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.2
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• pp.125-130
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• 2021

Implementation of livestock smart-farm can be done more effectively with IoT technology developing. An build of useful stock management system can be possibile if push messages of these judgement are notified on smart-phone after cattle's illness and estrus are judged using IoT technology. These judgement method of cattle's illness and estrus can be done with gathering living stock data using temperature sensor and 3 axis acceleration sensor and sending these data using IoT and internet network into server, and studying AI machine learning using these data. In this paper, to build this cattle management system based on IoT, effective system of the whole architecture is showed. Also an effective analysis and design method to develop this system software will be presented by showing user requirement analysis using object-oriented method, flowchart and screen design.