• Journal of Digital Convergence
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• v.16 no.9
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• pp.405-412
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• 2018

When a film company decides whether to invest or not in a scenario is the appropriate time to predict box office success. In response to market demands, AI based scenario analysis service has been launched, yet the algorithm is by no means perfect. The purpose of this study is to present a prediction model of movie scenario's box office hit based on human brain processing mechanism. In order to derive patterns of visual, auditory, and cognitive stimuli on the time spectrum of box office animation hit, this study applied Weber's law and brain mechanism. The results are as follow. First, the frequency of brain stimulation in the biggest box office movies was 1.79 times greater than that in the failure movies. Second, in the box office success, the cognitive stimuli codes are spread evenly, whereas in the failure, concentrated among few intervals. Third, in the box office success movie, cognitive stimuli which have big cognition load appeared alone, whereas visual and auditory stimuli which have little cognitive load appeared simultaneously.

• Advanced Industrial SCIence
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• v.1 no.1
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• pp.28-34
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• 2022

Many studies have been conducted on software quality evaluation. Recently, as artificial intelligence-related software has been developed a lot, research on methods for evaluating artificial intelligence functions in existing software is being conducted. Software evaluation has been based on eight quality characteristics: functional suitability, reliability, usability, maintainability, performance efficiency, portability, compatibility, and security. Research on the part that needs to be confirmed through evaluation of the function of the intelligence part is in progress. This study intends to introduce the contents of the evaluation method in this part. We are going to propose a quality evaluation method for artificial intelligence software by presenting the existing software quality evaluation method and the part to be considered in the AI part.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.2
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• pp.207-212
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• 2022

The rail transport system is developing into a smart railroad that pursues intelligence beyond the automation stage of each component in recent years. Smart railways based on ICT (: Information & Communications Technology) technologies such as IoT (: Internet of Things), big data, deep learning, AI (: Artificial Intelligence), and block chain are expected to cause many developmental changes in domestic and foreign railway technologies. In this paper, we look at the domestic and international standardization trends of railway communication technology, which forms the basis of such smart railway system, and discuss the direction for train control technology(CBTC) in Korea's railway transportation system to become a leading technology(UBTC) in the world railway industry in the future.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.233-241
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• 2023

This study analyzes the construction status of digital employment services in Korea and presents the direction of continuous advancement and development of digital employment services based on overseas cases and the latest digital technology development trends. Find out the specific digitalization promotion strategies and current status of major countries such as Belgium, Australia, the United Kingdom, Germany, France, and the United States. In addition, in order to present a plan for the development of digital employment services in Korea, we will propose a plan to expand digital employment services to online employment centers through individual and customized employment services, data openness, and expansion of public-private collaboration through digital employment services using AI and big data.

• Journal of Integrative Natural Science
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• v.17 no.3
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• pp.105-112
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• 2024

In the present age of artificial intelligence and metaverse, research on the importance of data and the amount of data is actively being conducted. Among these data, medical data contains the most sensitive information of individuals, so research on data generation, storage, management, and disposal is urgently needed. This study analyzed the status of medical data management in the United States, Europe, and Korea, and identified and analyzed medical data management laws and implementation status through working-level staff working in medical sites. As a result of the analysis, about 70% of medical professionals were able to identify the absence of recognition and management of medical data. The survey subjects were limited to Gwangju and Jeollanam-do, and 237 medical workers were conducted. More than 54% of the awareness of medical record generation, storage, and management came out, but about 70% of the occupations except doctors, oriental doctors, and dentists did not recognize the medical record management method. As necessary for medical record management, cost and the need for professional managers were 91.4%. Through this study, it was confirmed that the expansion of legal education for medical workers, the enactment of related laws, and the need for sincere fostering of medical record managers were required.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.173-178
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• 2020

In the near future, as artificial intelligence and computing network technology develop, collaboration with artificial intelligence (AI) will become important. In an AI society, the ability to communicate and collaborate among people is an important element of talent. To do this, it is necessary to understand how artificial intelligence based on computer science works. An algorithmic education focused on problem solving and learning is efficient for computer science education. In this study, the results of an assessment of computational thinking at the beginning of the semester, a satisfaction survey at the end of the semester, and academic performance were compared and analyzed for 28 students who received algorithmic education focused on problem-solving learning. As a result of diagnosing students' computational thinking and problem-solving learning, teaching methods, lecture satisfaction, and other environmental factors, a correlation was found, and regression analysis confirmed that problem-solving learning had an effect on improving lecture satisfaction and computational thinking ability. For algorithmic education, if you pursue a problem-solving learning technique and a way to improve students' satisfaction, it will help students improve their problem-solving skills.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.27 no.11
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• pp.29-38
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• 2022

This paper proposes a system for classifying gait types using an ensemble deep learning network for gait data measured by a smart insole equipped with multi-sensors. The gait type classification system consists of a part for normalizing the data measured by the insole, a part for extracting gait features using a deep learning network, and a part for classifying the gait type by inputting the extracted features. Two kinds of gait feature maps were extracted by independently learning networks based on CNNs and LSTMs with different characteristics. The final ensemble network classification results were obtained by combining the classification results. For the seven types of gait for adults in their 20s and 30s: walking, running, fast walking, going up and down stairs, and going up and down hills, multi-sensor data was classified into a proposed ensemble network. As a result, it was confirmed that the classification rate was higher than 90%.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.82-104
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• 2023

This study analyzes the effect of users' experiences using AI chatbot services on relationship quality and behavioral intention. For the study, a survey was conducted on users who experienced AI chatbot services, and the research hypothesis was verified by analyzing the final 299 copies of valid data. As a result of the analysis, it was confirmed that satisfaction and trust, which are the relationship quality dimensions of AI chatbot service, were formed in users through the cognitive experience, emotional experience, and relational experience. In addition, it was confirmed that satisfaction and trust have a positive effect on the intention to continue using and recommending AI chatbot services, which correspond to the level of consumers' behavioral intentions, respectively. In addition, in terms of relationship quality, it was significant in all paths of the road of behavior, but in satisfaction, the path coefficient of the road of continuous use of AI chatbot and recommended road was significantly higher than the path coefficient in trust. This study provided a theoretical foundation that the relationship with relationship quality that affects behavioral intention also affects AI chatbot services in the online environment, and it is significant in that it suggests that relationship quality is an important mediating factor in establishing long-term relationships with consumers.