• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.143-150
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• 2021

In the recent power industry, a change is taking place from manual meter reading to remote meter reading using AMI(Advanced Metering Infrastructure). If such the power data generated from the AMI is recorded on the blockchain, integrity is guaranteed by preventing forgery and tampering. As data sharing becomes transparent, new business can be created. However, Ethereum blockchain is not suitable for processing large amounts of transactions due to the limitation of processing speed. As a solution to overcome such the limitation, various On/Off-Chain methods are being investigated. In this paper, we propose a interface server using data sharding as a solution for storing large amounts of power data in Etherium blockchain environments. Experimental results show that our power-data processing system with sharding method lessen the data omission rate to 0% that occurs when the transactions are transmitted to Ethereum and enhance the processing speed approximately 9 times.

• Journal of Digital Convergence
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• v.19 no.11
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• pp.359-364
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• 2021

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

• The Journal of the Convergence on Culture Technology
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• v.8 no.3
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• pp.25-31
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• 2022

This study aims to investigate the degree of influence of LMS on online classes conducted after COVID-19 at C University in Gyeongsangnam-do, and to examine the relationship between overall class satisfaction and LMS satisfaction variables. C University started the project to construct 'Next-generation Smart LMS' and operated it from the first semester of 2021. As a result of conducting a survey about overall class satisfaction and LMS satisfaction consisting of five variables with 140 learners who have experienced online classes at C University. The learners showed high satisfaction not only in overall classes but also LMS. As a result of regression analysis of LMS satisfaction and overall class satisfaction, it was found that the functional convenience of LMS and interaction with instructors had a significant effect on overall class satisfaction. This study has limitations in that it was conducted only at one college and a limited number of variables were measured.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.5
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• pp.77-88
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• 2022

This study aims to understand the relationship between the distribution of fish species in the two water ecosystems and the habitat factors according to the survey period targeting Gwanggyo Lake Park in the city. There are studies on the appearance and distribution of species by applying eDNA to freshwater ecosystems. However, in the domestic, streams are the target, and studies on the relationship between species distribution and habitat environment in two water environments are lacking. We conducted to analyze the species list and relationship with habitat factors using eDNA research in May and October at 21 points in Gwanggyo Lake Park, Suwon City, which were connected to lakes and streams. As a result, there was no species difference in the water environment according to the survey period. However, the total number of reads during the spawning season(May) was 3,126,482, which was more than double that after the spawning season(October). Tolerant species appeared in Woncheon Lake with a slow or stagnant flow, but there was no significant correlation between species and habitat factors depending on the survey period. On the other hand, intermediate and sensitive species appeared in the Woncheon stream with high flow. There was a significant correlation between the low temperature during the spawning season and the high dissolved oxygen content after the spawning season(P<0.001, Tem.: 20.7±2.6℃, DO: 8.6±1.7). It is expected that environmental DNA will be used to survey species and suggest monitoring methods according to the survey period.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.117-122
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• 2022

In this study, Electromagnetic Induction Power Generation (EMG) is a structure consisting of a stator and a permanent magnet rotor, and is a method that enables power generation by using the kinetic energy of the human arm. Among them, the axial flux permanent magnet (AFPM) technique is a method that can act sensitively to the kinetic energy of the arm at a slow speed of the human body, and has a simple structure and can be designed and manufactured with an ultra-small size. Under the conditions of size of ø46×11mm, rotation speed of 7Hz (420rpm), output voltage 0.4VAC, output current 4.5mA, and output power 30mW were measured and analyzed the same as the target specification. Therefore, the purpose of this study is to study the power generation of the pendulum applying the AFPM (Axial Flux Permanent Magnet) technique to charge power to smart devices with kinetic energy of the human body.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.5
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• pp.67-77
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• 2022

Recently, the trend in structural analysis and design is moving towards the development of reliable system. The reliability-based method defines various limit states related to usability and failure, thereby enabling multiple levels of design according to the importance of a structure. Meanwhile, an actual structure is composed of a set of several elements, and particularly, a frame type is composed of a system in which the members are connected each other. At this time, the actual connection between members is in a semi-rigid condition, not in complete rigid or hinged. This semi-rigid is found in several structures, especially in agricultural facilities designed with lightweight materials. In this study, a system reliability analysis technique for frame structure was established, and applied to an analysis of the semi-rigid connection. Various conditions of correlation were applied to reflect the connectivity between members, and through this, the limitations of existing structural analysis method and the behavioral characteristics of structure were analyzed. The failure probability of the frame member component and the overall structure system was significantly different in consideration of the semi-rigid connection. In addition, it was evaluated that the behavior of structure can be more accurately analyzed if the correlation according to the position of members in a system is further investigated.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.315-320
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• 2021

While blockchain platforms for various purposes have been developed and the blockchain ecosystem is being developed, interoperability problems are emerging in which each blockchain is isolated and operated. In this study, we introduce interchain and sidechain technologies, which are blockchain that connect blockchain, and explain examples of using heterogeneous blockchain transactions and functions by applying them. In addition, blockchain, artificial intelligence, and IoT technologies, which are drawing attention in the fourth industrial revolution, are going through a process of converging and developing beyond their own development. In this regard, we present processes for combining artificial intelligence or IoT in blockchain, and propose a model that can operate without intervention by applying the combination of blockchain and artificial intelligence IoT to processes for trading and exchange between heterogeneous blockchain.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.489-500
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• 2021

The purpose of this study was to identify the effect of nursing college students' knowledge and attitudes about patient safety on self-efficacy. The participants comprised 186 students in a nursing college. Data collection was from September 22, 2020, to October 24, 2020. The data were analyzed by descriptive statistics, an independent t-test, a one-way ANOVA, the Pearson's correlation coefficient, and multiple regression through the SPSS 25.0 Program. Nursing students' knowledge of patient safety was 10.62±1.86 points on average, out of a total of 16 points. Attitudes toward patient safety and self-efficacy were an average of 3.72±0.45 points and an average of 3.71±0.65 points, respectively, out of 5 points. The relationship between knowledge and attitude about patient safety and self-efficacy was positively correlated. The effect on self-efficacy is attitude toward patient safety, satisfaction with a major, school practice, and clinical experience. The explanatory power was 14.4%. In conclusion, it is necessary to develop educational programs for patient safety that apply smart technology in order to enhance the self-efficacy of nursing students.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.207-214
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• 2021

Humans are environmentally exposed to various electromagnetic fields, but the evaluation of the harmfulness of electromagnetic field and the development of a system therefor are still incomplete. We aimed to develop a system for evaluating biohazard against electromagnetic fields, and to determine biohazard through the system. An extremely-low frequency magnetic field generator was designed and manufactured, and the output reliability of the device was verified. Using this device, the effect on the formation of cellular stress-granules and the cell cycle progression of cells exposed to high magnetic fields of 6 mT and 60 Hz was confirmed. As a result, exposure to high magnetic fields of 6 hr, 12 hr and 36 hr did not affect the formation of cell stress-induced granules and the cell division cycle. These results are an important basis for the determination of biohazard to the extremely-low frequency high magnetic field.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.910-916
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• 2021

In modern society, various digital communication equipment is being used due to the influence of the 4th industrial revolution. Accordingly, interest in removing noise generated in a data transmission process is increasing, and research is being conducted to efficiently reconstruct an image. In this paper, we propose a filtering algorithm to remove the AWGN generated in the digital image transmission process. The proposed algorithm classifies pixels with high similarity by selecting regions with similar patterns around the input pixels according to block matching to remove the AWGN that appears strongly in the image. The selected pixel determines the estimated value by applying the weight obtained by the local steering kernel, and obtains the final output by adding or subtracting the input pixel value according to the standard deviation of the center mask. In order to evaluate the proposed algorithm, it was simulated with existing AWGN removal algorithms, and comparative analysis was performed using enlarged images and PSNR.