• Journal of Information Processing Systems
• /
• v.18 no.1
• /
• pp.26-47
• /
• 2022

Chatbots were an important research subject in the past. A chatbot is a computer program or an artificial intelligence program that participates in a conversation via auditory or textual methods. As the research on chatbots progressed, some important issues regarding them changed over time. Therefore, it is necessary to review the technology with a focus on recent advancements and core research technologies. In this paper, we introduce five different chatbot technologies: natural language processing, pattern matching, semantic web, data mining, and context-aware computer. We also introduce the latest technology for the chatbot researchers to recognize the present situation and channelize it in the right direction.

• Electronics and Telecommunications Trends
• /
• v.39 no.2
• /
• pp.43-53
• /
• 2024

The media environment is rapidly evolving to be tailored to viewers using personal mobile devices in accordance with technological evolution and changes in social structures. Broadcast media technology is also advancing to enable new services, including data casting, in various reception environments beyond the existing fixed environment and one-way audio/video content services. In addition, technologies to increase the transmission capacity to accommodate next-generation large-capacity media content as well as communication network utilization and convergence technologies are being developed to facilitate interactive services and expand the broadcasting coverage. We discuss the current status and future prospects in broadcasting technology for terrestrial and mobile communication systems and analyze broadcasting technology elements for upcoming media environments relying on generative artificial intelligence.

• The Journal of the Korea Contents Association
• /
• v.20 no.9
• /
• pp.439-447
• /
• 2020

Public data refers to all data or information created by public institutions, and public information that leads to communication and cooperation among all people. Public data is an important method to lead the next generation of new industries such as artificial intelligence and smart cities, Korea is continuously ranked high in the international evaluation related to public data. However, despite the continuous efforts, the use of public data or industrial influence is insufficient. Quality issues are continuously discussed in the use of public data, but the criteria for quantitatively evaluating data are insufficient. This paper reviews indicators for public data quality evaluation and performs quantitative evaluation on selected public data. In particular, the quality of open standard data constructed and opened based on public data management guidelines is examined to determine whether government guidelines are appropriate. The data quality assessment includes the metadata and data values of open standard data, and is reviewed based on completeness and accuracy indicators. Based on the data analysis results, this paper proposes policy and technical measures for quality improvement.

• Tunnel and Underground Space
• /
• v.32 no.3
• /
• pp.173-193
• /
• 2022

In this study, international standardization trend of smart mining technology was analyzed focusing on the guidelines developed by GMG (Global Mining Guidelines Group). GMG is a non-profit organization that unites the global mining community. It was established to promote mining safety, innovation and sustainability. Currently, GMG's working group consists of artificial intelligence, asset management, autonomous mining, cybersecurity, data access and usage/interoperability, the electric mine, mineral processing, underground mining, and sustainability. Guideline development projects related to smart mining technology are being conducted in artificial intelligence, autonomous mining, cybersecurity, data access and usage/interoperability, and underground mining. As of April 2022, eight types of smart mining-related guidelines have been published through pre-launch, launch, guideline definition, contents generation, technical editing/layout/final review, and voting process. It is judged that the GMG guidelines can be an important reference for the development of domestic smart mining technology standards.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.37-53
• /
• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• Journal of the Korea Convergence Society
• /
• v.12 no.10
• /
• pp.63-70
• /
• 2021

Generating a super-resolution meteological data by using a high-resolution deep neural network can provide precise research and useful real-life services. We propose a new technique of generating improved training data for super-resolution deep neural networks. To generate high-resolution meteorological data with domain specific knowledge, Lambert conformal conic projection and objective analysis were applied based on observation data and ERA5 reanalysis field data of specialized institutions. As a result, temperature and humidity analysis data based on domain specific knowledge showed improved RMSE by up to 42% and 46%, respectively. Next, a super-resolution generative adversarial network (SRGAN) which is one of the aritifial intelligence techniques was used to automate the manual data generation technique using damain specific techniques as described above. Experiments were conducted to generate high-resolution data with 1 km resolution from global model data with 10 km resolution. Finally, the results generated with SRGAN have a higher resoltuion than the global model input data, and showed a similar analysis pattern to the manually generated high-resolution analysis data, but also showed a smooth boundary.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.8
• /
• pp.355-364
• /
• 2023

As advanced cyber threats continue to increase in recent years, it is difficult to detect new types of cyber attacks with existing pattern or signature-based intrusion detection method. Therefore, research on anomaly detection methods using data learning-based artificial intelligence technology is increasing. In addition, supervised learning-based anomaly detection methods are difficult to use in real environments because they require sufficient labeled data for learning. Research on an unsupervised learning-based method that learns from normal data and detects an anomaly by finding a pattern in the data itself has been actively conducted. Therefore, this study aims to extract a latent vector that preserves useful sequence information from sequence log data and develop an anomaly detection learning model using the extracted latent vector. Word2Vec was used to create a dense vector representation corresponding to the characteristics of each sequence, and an unsupervised autoencoder was developed to extract latent vectors from sequence data expressed as dense vectors. The developed autoencoder model is a recurrent neural network GRU (Gated Recurrent Unit) based denoising autoencoder suitable for sequence data, a one-dimensional convolutional neural network-based autoencoder to solve the limited short-term memory problem that GRU can have, and an autoencoder combining GRU and one-dimensional convolution was used. The data used in the experiment is time-series-based NGIDS (Next Generation IDS Dataset) data, and as a result of the experiment, an autoencoder that combines GRU and one-dimensional convolution is better than a model using a GRU-based autoencoder or a one-dimensional convolution-based autoencoder. It was efficient in terms of learning time for extracting useful latent patterns from training data, and showed stable performance with smaller fluctuations in anomaly detection performance.

• The Journal of Bigdata
• /
• v.8 no.2
• /
• pp.165-175
• /
• 2023

LNG is a representative imported cargo at Incheon Port and has a relatively high contribution to the increase/decrease in overall cargo volume at Incheon Port. In addition, in the view point of nationwide, LNG is the one of the most important key resource to supply the gas and generate electricity. Thus, it is very essential to identify the factors that have impact on the demand fluctuation and build the appropriate forecasting model, which present the basic information to make balance between supply and demand of LNG and establish the plan for power generation. In this study, different to previous research based on macroscopic annual data, the weekly demand of LNG is converted from the cargo volume unloaded by LNG carriers. We have identified the periodicity and correlations among internal and external factors of demand variability. We have identified the input factors for predicting the LNG demand such as seasonality of weekly cargo volume, the peak power demand, and the reserved capacity of power supply. In addition, in order to predict LNG demand, considering the characteristics of the data, time series prediction with weekly LNG cargo volume as a dependent variable and prediction through an artificial neural network model were made, the suitability of the predictions was verified, and the optimal model was established through error comparison between performance and estimates.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.11
• /
• pp.329-331
• /
• 2020

Edutech is a compound word of education and technology, and is an educational paradigm in the era of the 4th industrial revolution. This refers to next-generation education using information and communication technology (ICT) such as big data, artificial intelligence (AI), robots, and virtual reality (VR) of the 4th industrial revolution. e-Learning is being used as an online lecture for education in ICT, but edutech is attracting attention along with e-learning as the feeding of non-face-to-face education has rapidly increased due to COVID-19. Therefore, this paper summarizes the reviewed papers on the blockchain-based badge service platform, simulation-based collaborative e-Learning system, video English dictionary, and blockchain-based access control audit system.

• Annual Conference of KIPS
• /
• 2019.10a
• /
• pp.1023-1025
• /
• 2019

Coal and Natural gas are two biggest contributors to a generation of energy throughout the world. Most of these resources create environmental pollution while making energy affecting the natural habitat. Many approaches have been proposed as alternatives to these sources. One of the leading alternatives is Solar Energy which is usually harnessed using solar farms. In artificial intelligence, the most researched area in recent times is machine learning. With machine learning, many tasks which were previously thought to be only humanly doable are done by machine. Neural networks have two major subtypes i.e. Convolutional neural networks (CNN) which are used primarily for classification and Recurrent neural networks which are utilized for time-series predictions. In this paper, we predict energy generated by solar fields and optimal angles for solar panels in these farms for the upcoming seven days using environmental and historical data. We experiment with multiple configurations of RNN using Vanilla and LSTM (Long Short-Term Memory) RNN. We are able to achieve RSME of 0.20739 using LSTMs.