• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.825-832
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• 2022

In this paper, we discuss the design and implementation of predictive and diagnostic models for realizing intelligent predictive models by collecting and storing the power output of agricultural photovoltaic power generation systems. Our model predicts the amount of photovoltaic power generation using RNN, LSTM, and GRU models, which are recurrent neural network techniques specialized for time series data, and compares and analyzes each model with different hyperparameters, and evaluates the performance. As a result, the MSE and RMSE indicators of all three models were very close to 0, and the R2 indicator showed performance close to 1. Through this, it can be seen that the proposed prediction model is a suitable model for predicting the amount of photovoltaic power generation, and using this prediction, it was shown that it can be utilized as an intelligent and efficient O&M function in an agricultural photovoltaic system.

• Journal of Integrative Natural Science
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• v.15 no.3
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• pp.99-110
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• 2022

Solar photovoltaic (PV) system shows a non-linear current (I) -voltage (V) characteristics, which depends on the surrounding environment factors, such as irradiance, temperature, and the wind. Solar PV system, with current (I) - voltage (V) and power (P) - Voltage (V) characteristics, specifies a unique operating point at where the possible maximum power point (MPP) is delivered. At the MPP, the PV array operates at maximum power efficiency. In order to continuously harvest maximum power at any point of time from solar PV modules, a good MPPT algorithms need to be employed. Currently, due to its simplicity and easy implementation, Perturb and Observe (P&O) algorithms are the most commonly used MPPT control method in the PV systems but it has a drawback at suddenly varying environment situations, due to constant step size. In this paper, to overcome the difficulties of the fast changing environment and suddenly changing the power of PV array due to constant step size in the P&O algorithm, least mean Square (LMS) methods is proposed together with P&O MPPT algorithm which is superior to traditional P&O MPPT. PV output power is predicted using LMS method to improve the tracking speed and deduce the possibility of misjudgment of increasing and decreasing the PV output. Simulation results shows that the proposed MPPT technique can track the MPP accurately as well as its dynamic response is very fast in response to the change of environmental parameters in comparison with the conventional P&O MPPT algorithm, and improves system performance.

• Journal of Engineering Education Research
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• v.25 no.5
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• pp.85-93
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• 2022

Capstone Design, a project-based learning technique, is the most important curriculum that clarifying major knowledge and cultivating the ability to apply through the process of solving problems in the industrial field centered on the student project team. Accordingly, various and extensive studies are being conducted for the successful implementation of capstone design courses. Unlike previous studies, this study aimed to quantitatively analyze the opinions that recorded the experiences and feelings of students who performed capstone design, and used text mining methodologies such as frequency analysis, correlation analysis, topic modeling, and sentiment analysis. As a result of examining the overall opinions of the latter period through frequency analysis and correlation analysis, there was a difference between the languages used by the students in the opinions according to gender and project results. Through topic modeling analysis, 'topic selection' and 'the relationship between team members' showed an increase in occupancy or high occupancy, and topics such as 'presentation', 'leadership', and 'feeling what they felt' showed a tendency to decreasing occupancy. Lastly, sentiment analysis has found that female students showed more neutral emotions than male students, and the passed group showed more negative emotions than the non-passed group and less neutral emotions. Based on these findings, students' practical recognition of the curriculum was considered and implications for the improvement of capstone design were presented.

• Environmental and Resource Economics Review
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• v.31 no.3
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• pp.343-365
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• 2022

This paper tries to identify the effect of reducing PM2.5 concentration of the First Seasonal Management Policy implemented by Korean government by using statistical method. In particular, this paper tests the hypothesis that this policy effect may differ by region (west-coast, south-coast, and east-coast). To this end, this paper analyzed only pure policy effects by removing temporal abnormalities such as COVID-19, warm winter temperature during the policy implementation period (December 2019 to March 2020) by using the difference-in-difference method (DID). As a result of the analysis, this policy had the effect of reducing PM2.5, but the effect is not homogenous by region. In particular, PM2.5 reducing effect is the largest in west-coast region and south-coast region folllows, but its effect is not statistically significant in the east-cost region. In conclusion, this paper drew implications that the current Seasonal mamangement policy which is implemented regardless of the regional difference needs to be changed.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.169-174
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• 2022

The purpose of the study is to research the legal nature and essence of corrupt behavior, as well as the international and national legal aspects of the fight against corruption. The article discloses the relation between the factual results of the operation of anti-corruption normative and legal acts and the goals and objectives for which they were adopted. The effectiveness of the regulatory effect and quality of anti-corruption legislation is determined by the example of the Russian Federation. The article provides an analysis of theoretical aspects of the theory and history of the formation and development of anti-corruption legislation (on the example of Russia and some other countries, as well as international legal norms) giving several practical examples from foreign legislation demonstrating the structure of the system of government bodies battling against corrupt behavior (including its latent forms). The authors suggest that there is a need for a unified conception of information and propaganda support of state anti-corruption activities. This will make it possible to inform the population that the state is actively working to prevent corruption threats and to bring perpetrators to justice, as well as contribute to citizens' trust in the state policy in this area. At the same time, it is necessary to regularly inform the citizens about the provisions of the anti-corruption legislation, explaining the importance of their observance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1832-1837
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• 2022

In the SW education of non-majors who encounter liberal arts education experience difficulties in the SW development environment and understanding they encounter for the first time, relevance to their major, and convergence thinking ability. In order to compensate for the difficulties of non-major learners in liberal arts education, a relatively easily accessible software was used to utilize a demonstration-oriented model that can be applied to beginners in SW education. In order to understand the logical flow of applications and problem solving used in real life, we proposed a convergence SW teaching method that combines repeated implementation through demonstration by the instructor and imitation of the learner, and learning indicators to increase the learning satisfaction and achievement of the learner. In the experiment applying the teaching and learning method proposed in this paper, meaningful results were shown when evaluating the learning effect, academic achievement, learning satisfaction, and teaching and learning method aspects of SW education.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1809-1815
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• 2022

A digital twin is a technology that creates a virtual world identical to the real world. Problems in the real world can be identified through various simulations, so it is a trend to be applied in various industries. In order to apply the digital twin, it is necessary to analyze the drawings in which the structure of the real world to be made identical is designed. Although the technology for analyzing drawings is being studied, it is difficult to apply them because the rules or standards for drawing drawings are different for each author. Therefore, in this paper, we implement a system that analyzes and classifies the vertex detail, one of the drawings, using artificial intelligence. Through this, we intend to confirm the possibility of analyzing and classifying drawings through artificial intelligence and introduce future research directions.

• Journal of Software Assessment and Valuation
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• v.17 no.1
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• pp.11-16
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• 2021

The importance of software is increasing due to the development of information and communication, and software copyright disputes are also increasing. In this paper, the source of the submitted programs and the files necessary for the execution of the program were taken as the scope of analysis. The large-capacity file transfer solution program to be analyzed provides additional functions such as confidentiality, integrity, user authentication, and non-repudiation functions through digital signature and encryption of data.In this paper, we analyze the program A, program B, and the program C. In order to calculate the program similarity rate, the following contents are analyzed. Analyze the similarity of the package structure, package name, source file name in each package, variable name in source file, function name, function implementation source code, and product environment variable information. It also calculates the overall similarity rate of the program. In order to check the degree of agreement between the package structure and the package name, the similarity was determined by comparing the folder structure. It also analyzes the extent to which the package structure and package name match and the extent to which the source file (class) name within each package matches.

• Archives of Plastic Surgery
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• v.48 no.4
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• pp.361-365
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• 2021

Background Pain in the postoperative body contouring patient has traditionally been managed with narcotic medication. In an effort to minimize side effects and prevent addiction, plastic surgeons are searching for novel ways to provide adequate analgesia, one of which is nerve blocks. This study was conducted with a meta-analysis that evaluates the efficacy of these blocks for patients who undergo breast surgery. Methods A search of the PubMed/MEDLINE database for articles including the terms "post-operative analgesia" OR "postoperative pain management" AND "in plastic surgery" OR "in cosmetic surgery" OR "in elective surgery" in February 2019 generated five studies on elective breast augmentation and reduction mammoplasty that reported pain scores and quantities of opioids consumed. Independent samples t-tests, one-way analysis of variance, and a random effects model were implemented for evaluation. Results A total of 317 patients were identified as having undergone body contouring of the breast, about half of which received a nerve block. Pain scores on a 1-10 scale and opioid dose-equivalents were calculated. Those who were blocked had an average score of 2.40 compared to 3.64 for those who did not (P<0.001), and required an average of 5.20 less narcotic doses (P<0.001). Pain relief following subpectoral augmentation was best achieved with type-II blocks as opposed to type-I and type-II with serratus plane (P<0.001). Conclusions The opioid epidemic has extended to all surgical specialties. Implementation of a nerve block seems to be an efficacious and cost-effective mechanism to not only help with post-operative pain, but also lower the need for narcotics, especially in subpectoral augmentation.

• Journal of Convergence for Information Technology
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• v.11 no.5
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• pp.23-29
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• 2021

Recently, a situation in which a specific content service is impossible worldwide has occurred due to a failure of the platform service and a significant social and economic problem has been caused in the global service market. In order to secure the stability of platform services, intelligent platform operation management is required. In this study, big data flow management(BDFM) and implementation method were proposed to quickly detect to abnormal service status in the platform operation environment. As a result of analyzing, BDFM technique improved the characteristics of abnormal failure detection by more than 30% compared to the traditional NMS. The big data flow management method has the advantage of being able to quickly detect platform system failures and abnormal service conditions, and it is expected that when connected with AI-based technology, platform management is performed intelligently and the ability to prevent and preserve failures can be greatly improved.