• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.3
• /
• pp.123-128
• /
• 2019

This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.

• Journal of Broadcast Engineering
• /
• v.24 no.1
• /
• pp.67-76
• /
• 2019

In this paper, we propose an efficient object classification method based on semantic segmentation for multi-labeled image data. In addition to various pixel unit information and processing techniques such as color information, contour, contrast, and saturation included in image data, a detailed region in which each object is located is extracted as a meaningful unit and the experiment is conducted to reflect the result in the inference. We use a neural network that has been proven to perform well in image classification to understand which object is located where image data containing various class objects are located. Based on these researches, we aim to provide artificial intelligence services that can classify real-time detailed areas of complex images containing various objects in the future.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.1
• /
• pp.171-178
• /
• 2019

Korea's shipbuilding industry is in a critical condition due to changes in the domestic and international environment. To overcome this crisis, preemptive development of products and technologies through prediction of new demand for ships is necessary. The goal of this research is to develop an artificial intelligence algorithm based on ship big data in order to predict new demand for ships. We intend to develop a big data analytics platform specialized in predicting ship demand and to utilize the forecast results of new ship demand through data analysis for planning/development of new products. By doing so, the development of sustainable new business models for equipment and equipment manufacturers will create new growth engines for shipyard and shipbuilders. Furthermore, it is expected that shipbuilders will be able to create business cases based on measurable performance, plan market-oriented products and services, and continuously achieve innovation that has high market destructive power.

• Journal of The Korean Association of Information Education
• /
• v.25 no.5
• /
• pp.751-759
• /
• 2021

This study developed an artificial intelligence education program using localized public data as an educational method for improving computing thinking skills of elementary school students. According to the ADDIE model, the program design was carried out based on the results of pre-requisite analysis for elementary school students, and textbooks and education programs were developed. Based on localized public data, the training program was constructed to learn the principles of artificial intelligence using machine learning for kids and scratches and to solve problems and improve computational thinking through abstracting public data for purpose. It is necessary to put this training program into the field through further research and verify the change in students' computational thinking as a result.

• Journal of The Korean Association of Information Education
• /
• v.25 no.5
• /
• pp.859-869
• /
• 2021

The purpose of this study was to develop an AI liberal arts subject for pre-service teachers of all majors in elementary and middle school. To this end, the main areas of the AI curriculum, sub-themes for each week, and activities were specifically designed and verified through two rounds of Delphi by 13 experts. The AI liberal arts curriculum in this study consists of three areas. Then, detailed topics for each week were confirmed, and a total of 13 weeks were arranged according to the natural flow. It was intended to help teaching and learning by arranging learning activities based on the experiential learning model according to the learning content. It is expected that this study will be used as basic data for the development of various AI education subjects for pre-service teachers in elementary and secondary schools in the future.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.9
• /
• pp.13-25
• /
• 2021

In the age of digital innovation based on the Internet, Information and Communication and Artificial Intelligence technologies, huge amounts of datasets are being generated, collected, accumulated, and opened on the web by various public institutions providing useful and public information. In order to analyse, gain useful insights and information from data, Formal Concept Analysis(FCA) has been successfully used for analyzing, classifying, clustering and visualizing data based on the binary relation between objects and attributes in the dataset. In this paper, we present an approach for enhancing the analysis of relational attributes of data within the extended framework of FCA, which is designed to classify, conceptualize and visualize sets of objects described not only by attributes but also by relations between these objects. By using the proposed tool, RCA wizard, several experiments carried out on some public open datasets demonstrate the validity and usability of our approach on generating and visualizing conceptual hierarchies for extracting more useful knowledge from datasets. The proposed approach can be used as an useful tool for effective data analysis, classifying, clustering, visualization and exploration.

• Food Science and Industry
• /
• v.54 no.3
• /
• pp.124-131
• /
• 2021

In the 4th industrial revolution, Artificial Intelligence (AI), big data, Internet of Things (IoT) are already around us, making our society hyper-connected and blurring the lines between the digital and biological spheres. We witness drastic changes not only in the food industry, but also in economy, society and our life as a whole. Technologies bring industrial reorganization and greater changes at the system level and the food industry is not exceptional. Human demand for foods continues to grow and the very nature of the food industry remains unchanged, but its production, distribution and marketing face unprecedent innovations. Passing through the global pandemic, the food industry has been evolved into 'contact-free', as the safety become our top priority. Amid the gradual shift to technology-oriented society, the smarter food safety management skills and tools are being adopted in many countries exerting greater efforts to enhance traceability and to upgrade AI-powered safety management system.

• Plant Journal
• /
• v.17 no.3
• /
• pp.37-41
• /
• 2021

P&ID((Piping and Instrument Diagram) is a key drawing in the engineering industry because it contains information about the units and instrumentation of the plant. Until now, simple repetitive tasks like listing symbols in P&ID drawings have been done manually, consuming lots of time and manpower. Currently, a deep learning model based on CNN(Convolutional Neural Network) is studied for drawing object detection, but the detection time is about 30 minutes and the accuracy is about 90%, indicating performance that is not sufficient to be implemented in the real word. In this study, the detection of symbols in a drawing is performed using 1-stage object detection algorithms that process both region proposal and detection. Specifically, build the training data using the image labeling tool, and show the results of recognizing the symbol in the drawing which are trained in the deep learning model.

• International Journal of Computer Science & Network Security
• /
• v.21 no.4
• /
• pp.186-198
• /
• 2021

The introduction of digital technologies affects most socio-economic processes and activities in the economy, from agriculture to public services. Even though the world is currently only in the early stages of digital transformation, the digital economy is growing rapidly, especially in developing countries. Shortly, digital platforms will be able to replace the "invisible hand" of the market and turn it into digital. Some digital platforms have already reached global reach in some sectors of the economy. The growing value of data and artificial intelligence is reflected in the high capitalization of these enterprises. Their growing role has far-reaching consequences for the organization of economic activity and integration into the field of e-business. However, their importance and level of development in different countries differ significantly. The main purpose of this article is an assessment of the level and trends of the digital economy in the world and the identification of homogeneous groups of states following the main trends in the development of its components from among the EU countries. The methodology of the conducted research is based on the use of general scientific research methods in the analysis of secondary sources and the application of statistical methods of correlation-regression and cluster analysis. Macroeconomic indicators and components of DESI (Digital Economy and Society Index) were used for the analysis. Results. Based on the analysis established that most developed countries have a medium level of digitalization of the business environment and a high level of digitalization of socially oriented public services, while countries with lower GDP focus their policies on building digital infrastructure and training qualified personnel. The study summarizes and analyzes current trends in digital technology, analyzes the level and dynamics of integration of digital technologies of the studied EU countries, the level of development of e-business and e-commerce. The conceptualization of mechanisms of creation of added value in the digital economy is offered and the possible consequences of digitalization of the economy of developing countries are generalized.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.2
• /
• pp.41-47
• /
• 2021

Manufacturing is facing radical changes around the world. The manufacturing industry, which has been changing since Germany, is now being introduced, improved, and developed worldwide by manufacturers under the name of smart factory. By utilizing IT technologies such as artificial intelligence and cloud at the production site, the desire to break away from the past manufacturing environment is increasing. How these technologies will be efficient in the future, manufacturing worldwide now faces radical changes. The manufacturing industry, which has been changing since Germany, is now being introduced, improved, and developed worldwide by manufacturers under the name of smart factory. By utilizing IT technologies such as artificial intelligence and cloud at the production site, the desire to break away from the past manufacturing environment is increasing. Discussions continue on how these technologies can be used efficiently and effectively. Increasingly, the expansion of the range from factory areas to regions, countries, and around the world raises the need for international standards for interactions. In this paper, we propose a design and implementation method for managing facilities, sensors, etc. as assets and monitoring facility data collected through OPC UA.