• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.499-501
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• 2021

Recently, artificial intelligence and SW have occupied an important position worldwide as the foundation technology of the era of the 4th industrial revolution, and web browser-based programming learning systems are becoming common due to changes in the learning environment caused by COVID-19. In accordance with this trend, this paper proposes a functionally scalable microservice-based system structure for an online evaluation system as a tool for learning algorithms that are the basis of artificial intelligence and SW. In addition, a functional structure for applying machine learning to automatic evaluation functions under the proposed system structure is also proposed.

• Journal of Multimedia Information System
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• v.7 no.1
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• pp.33-40
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• 2020

Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is the Data mining task of inferring a function from labeled training data. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper demonstrates an overview of major technological standpoint and gratitude of the elementary development of speech recognition and provides impression method has been developed in every stage of speech recognition using supervised learning. The project will use DNN to recognize speeches using magnitudes with large datasets.

• Smart Media Journal
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• v.11 no.2
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• pp.39-52
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• 2022

In the context of the evolution of automation and intelligence, deep learning and machine learning algorithms have been widely applied in aquaculture in recent years, providing new opportunities for the digital realization of aquaculture. Especially, water quality management deserves attention thanks to its importance to food organisms. In this study, we proposed an end-to-end deep learning-based TabNet model for water quality prediction. From major indexes of water quality assessment, we applied novel deep learning techniques and machine learning algorithms in innovative fish aquaculture to predict the number of water cells counting. Furthermore, the application of deep learning in aquaculture is outlined, and the obtained results are analyzed. The experiment on in-house data showed an optimistic impact on the application of artificial intelligence in aquaculture, helping to reduce costs and time and increase efficiency in the farming process.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.37-43
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• 2021

This study proposed an effective automatic classification technique to identify dropout patterns of university students, and based on this, an intelligent recommender system to prevent dropouts. To this end, 1) a data processing method to improve the performance of machine learning was proposed based on actual enrollment/dropout data of university students, and 2) performance comparison experiments were conducted using five types of machine learning algorithms. 3) As a result of the experiment, the proposed method showed superior performance in all algorithms compared to the baseline method. The precision rate of discrimination of enrolled students was measured to be up to 95.6% when using a Random Forest(RF), and the recall rate of dropout students was measured to be up to 80.0% when using Naive Bayes(NB). 4) Finally, based on the experimental results, a method for using a counseling recommender system to give priority to students who are likely to drop out was suggested. It was confirmed that reasonable decision-making can be conducted through convergence research that utilizes technologies in the IT field to solve the educational issues, and we plan to apply various artificial intelligence technologies through continuous research in the future.

• The Journal of the Korea Contents Association
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• v.19 no.7
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• pp.526-537
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• 2019

This study researched the effect of application systems for media industry by using machine learning method focusing on industrial organization theory. First, for applying the system successfully, formation of sympathy about needs is required. The introduction of machine learning can bring change in each stage of value chain especially, decision making process of investment and production process. In investment side, objective performance prediction data can enhance efficiency, and content diversity can decrease with concentrated investment phenomenon to secured content by the system. In production side, if the system support to make creators decrease simple repeat works, production efficiency will increase.

• Journal of Digital Convergence
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• v.17 no.10
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• pp.265-271
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• 2019

The number of galleries across the country is increasing as interest in cultural life increases due to the increase in national income. However, museum satisfaction is relatively low compared to other services. In this paper, we propose a service that provides preference information based on machine learning in embedded system environment in order to increase museum satisfaction. The proposed algorithm implements an embedded system using Raspberry Pi. Machine learning was used to find works similar to the viewer's favorite works, and several models were compared to select models applicable to embedded systems. By using the preference information, it is possible to effectively organize the gallery exhibition contents to increase the exhibition satisfaction and the re-visit rate of the museum.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.3
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• pp.42-52
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• 2022

Bus Information System (BIS) services are expanding nationwide to small and medium-sized cities, including large cities, and user satisfaction is continuously improving. In addition, technology development related to improving reliability of bus arrival time and improvement research to minimize errors continue, and above all, the importance of information accuracy is emerging. In this study, accuracy performance was evaluated using LSTM, a machine learning method, and compared with existing methodologies such as Kalman filter and neural network. As a result of analyzing the standard error for the actual travel time and predicted values, it was analyzed that the LSTM machine learning method has about 1% higher accuracy and the standard error is about 10 seconds lower than the existing algorithm. On the other hand, 109 out of 162 sections (67.3%) were analyzed to be excellent, indicating that the LSTM method was not entirely excellent. It is judged that further improved accuracy prediction will be possible when algorithms are fused through section characteristic analysis.

• Journal of Information Technology Services
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• v.22 no.4
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• pp.97-110
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• 2023

In this study, we propose a technique to automatically generate transfer documents using sensor data from livestock manure transfer systems. The research involves analyzing sensor data and applying machine learning techniques to derive optimized outcomes for livestock manure transfer documents. By comparing and contrasting with existing documents, we present a method for automatic document generation. Specifically, we propose the utilization of Gradient Boosting, a machine learning algorithm. The objective of this research is to enhance the efficiency of livestock manure and liquid byproduct management. Currently, stakeholders including producers, transporters, and processors manually input data into the livestock manure transfer management system during the disposal of manure and liquid byproducts. This manual process consumes additional labor, leads to data inconsistency, and complicates the management of distribution and treatment. Therefore, the aim of this study is to leverage data to automatically generate transfer documents, thereby increasing the efficiency of livestock manure and liquid byproduct management. By utilizing sensor data from livestock manure and liquid byproduct transport vehicles and employing machine learning algorithms, we establish a system that automates the validation of transfer documents, reducing the burden on producers, transporters, and processors. This efficient management system is anticipated to create a transparent environment for the distribution and treatment of livestock manure and liquid byproducts.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.1-9
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• 2023

Malware files containing concealed malicious scripts have recently been identified within MS Office documents frequently. In response, this paper describes the design and implementation of a system that automatically detects malicious digital files using machine learning techniques. The system is proficient in identifying malicious scripts within MS Office files that exploit the OLE VBA macro functionality, detecting malicious scripts embedded within the CDH/LFH/ECDR internal field values through OOXML structure analysis, and recognizing abnormal CDH/LFH information introduced within the OOXML structure, which is not conventionally referenced. Furthermore, this paper presents a mechanism for utilizing the VirusTotal malicious script detection feature to autonomously determine instances of malicious tampering within MS Office files. This leads to the design and implementation of a machine learning-based integrated software. Experimental results confirm the software's capacity to autonomously assess MS Office file's integrity and provide enhanced detection performance for arbitrary MS Office files when employing the optimal machine learning model.